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COMAR 26.08.02.03-3 


Protection 


/-vyci iuy 


Region III 
Chesapeake Bay 
Program Office 


Region III 
Water Protection 
Division 


EPA 903-R-08-001 
CBP/TRS 290-08 
September 2008 


TD 225 
.043 
P48 
2008 
Copy 1 


In coordination with the Office of Water/Office of Science and Technology, Washington, D.C., and the states 
of Delaware, Maryland, New York, Pennsylvania, Virginia and West Virginia and the District of Columbia 








5s A 


Ambient Water Quality 
Criteria for Dissolved 
Oxygen, Water Clarity 
and Chlorophyll a for 
the Chesapeake Bay 
and Its Tidal Tributaries 

2008 Technical Support 
for Criteria Assessment 
Protocols Addendum 

September 2008 


f"£ ?/.,• 


N S F 






























Ambient Water Quality Criteria for 
Dissolved Oxygen, Water Clarity and 
Chlorophyll a for the Chesapeake Bay 
and Its Tidal Tributaries: 

2008 Technical Support for Criteria 
Assessment Protocols Addendum 


September 2008 UBRARY OF congress 

U.S. Environmental Protection Agency 
Region III 

Chesapeake Bay Program Office 
Annapolis, Maryland 

and 

Region III 

Water Protection Division 
Philadelphia, Pennsylvania 

in coordination with 

Office of Water 

Office of Science and Technology 
Washington, D.C. 

and 

the states of 

Delaware, Maryland, New York, 

Pennsylvania, Virginia, and 
West Virginia and the District of Columbia 


Library of Cong 



451314 


Ill 


Contents 

Acknowledgments . v 

I. Introduction . 1 

Literature Cited. 2 

II. 2008 92-Segment Scheme for the Chesapeake Bay 

Water Quality Criteria . 5 

Background. 5 

Chesapeake Bay Program Segmentation Schemes. 5 

2008 Chesapeake Bay and Tidal Tributaries 92-Segment Scheme. 6 

Unresolved Boundary for District of Columbia Upper Potomac River . . 12 
Literature Cited. 12 

III. Refinements to Procedures for Assessing Chesapeake Bay 

Dissolved Oxygen Criteria . 13 

Background. 13 

Dissolved Oxygen Criteria Assessment: Stations and Accepted Data ... 14 

Pycnocline Definition and Boundaries . 15 

Revising Designated Use Boundaries with Enhanced Pycnocline 
Definition Procedure . 15 

Calculation of Upper and Lower Pycnoclines for Dissolved 

Oxygen Designated Use Criteria Assessment. 15 

Literature Cited. 18 

IV. Refinements to Procedures for Assessing Chesapeake Bay 

Water Clarity and SAV Criteria . 19 

Background. 19 

Revision of the Water Clarity Acres Assessment Methodology . 20 

Clarification of Water Clarity Assessment Procedures. 21 

Statistical Model Revision. 21 

Converting Turbidity to K d for Calculation of Water Clarity Acres . . 21 
Interpolation Software and Approach . 24 

Literature Cited. 24 


Contents 























IV 


V. Chlorophyll a Criteria Assessment Procedures . 27 

Background. 27 

Approach and Protocol Application with Examples. 29 

Types of Output. 30 

Future Directions. 32 

Literature Cited. 33 

Acronyms . 34 

Appendices 

A. Procedure for Assessing Dissolved Oxygen Criteria Attainment: 

30-day Criterion, Including Plotting a Bioreference Curve. 35 

B. Stations Involved in the 2004-2006 303d Listing Assessment for 2008 .... 39 

C. A Comparison of Methods for Estimating K d . 44 

D. Derivation of K d Regressions: DATAFLOW Report on the Lumping 

vs. Splitting of Regions for MDDNR DATAFLOW K d vs. Turbidity 
Regressions and Calibrations. 47 

E. Chesapeake Bay Water Clarity Assessment Framework . 59 

F. Chesapeake Bay Clarity Criteria Attainment Results . 66 

G. Chlorophyll a Assessment Protocol. 70 


Contents 
















V 


Acknowledgments 


This fifth addendum to the EPA April 2003 publication of Ambient Water Quality 
Criteria for Dissolved Oxygen, Water Clarity, and Chlorophyll a for Chesapeake Bay 
and its Tidal Tributaries (Regional Criteria Guidance) was developed and documented 
through the collaborative efforts of the members of the Chesapeake Bay Program’s 
(CBP) Criteria Assessment Protocols Workgroup and Water Quality Steering 
Committee. 

PRINCIPAL AND CONTRIBUTING AUTHORS 

The document resulted from the collaborative expertise and talents of Chesapeake 
Bay Program’s state agency, federal agency and academic institutional partners. The 
following are principal and contributing authors of this addendum: Peter Tango, U.S. 
Geological Survey/Chesapeake Bay Program Office; Jeni Keisman, University of 
Maryland Center for Environmental Science/Chesapeake Bay Program Office; 
Richard Batiuk, U.S. EPA Region III Chesapeake Bay Program Office, Mark Trice, 
Maryland Department of Natural Resources; Frederick Hoffman, Virginia Department 
of Environmental Quality; Ken Moore, Virginia Institute of Marine Science; David 
Parrish, Virginia Institute of Marine Science; Tish Robertson, Virginia Department of 
Environmental Quality; Elgin Perry, Statistics Consultant; Gary Shenk, U.S. EPA 
Region III Chesapeake Bay Program Office. 

CRITERIA ASSESSMENT PROTOCOL WORKGROUP 

Peter Tango, Chair, U.S. Geological Survey; Cheryl Atkinson, United States 
Environmental Protection Agency; Harry Augustine, Virginia Department of 
Environmental Quality; Mark Barath, United States Environmental Protection 
Agency; Tom Barron, Pennsylvania Department of Environmental Protection; Richard 
Batiuk, United States Environmental Protection Agency; Stephen Cioccia, Virginia 
Department of Environmental Quality; Elleanor Daub, Virginia Department of 
Environmental Quality; Thomas Gardner, United States Environmental Protection 
Agency; Sherm Garrison, Maryland Department of Natural Resources; Darryl Glover, 
Virginia Department of Environmental Quality; John Hill, Maryland Department of 
the Environment; Rick Hoffman, Virginia Department of Environmental Quality; 
Larry Merrill, United States Environmental Protection Agency; Bruce Michael, 
Maryland Department of Natural Resources; Ken Moore, Virginia Institute of Marine 
Science; Shah Nawaz, District Department of the Environment; Roland Owens, 
Virginia Department of Environmental Quality; Jennifer Palmore, Virginia 
Department of Environmental Quality; Tom Parham, Maryland Department of Natural 


Acknowledgments 


VI 


Resources; Elgin Perry, Statistics Consultant; Charles Poukish, Maryland Department 
of the Environment; Tish Robertson, Virginia Department of Environmental Quality; 
Matt Rowe, Maryland Department of the Environment; John Schneider, Delaware 
Department of Natural Resources and Environmental Control; Susan Sciratta, United 
States Environmental Protection Agency; Gary Shenk, United States Environmental 
Protection Agency; Donald Smith, Virginia Department of Environmental Quality; 
Scott Stoner, New York State Department of Environmental Conservation; Matt 
Stover, Maryland Department of the Environment; Bryant Thomas, Virginia 
Department of Environmental Quality; Mark Trice, Maryland Department of Natural 
Resources; Howard Weinberg, University of Maryland Center for Environmental 
Science; David Wolanski, Delaware Department of Natural Resources and 
Environmental Control. 

WATER QUALITY STEERING COMMITTEE 

Diana Esher, Chair, United States Environmental Protection Agency; Richard Batiuk, 
United States Environmental Protection Agency; Sheila Besse, District of Columbia 
Department of the Environment; William Brannon, West Virginia Department of 
Environmental Protection Division of Water and Waste Management; Patricia 
Buckley, Pennsylvania Department of Environmental Protection; Katherine Bunting- 
Howarth, Delaware Department of Natural Resources and Environmental Control; 
Monir Chowdhury, District Department of the Environment; Ron Entringer, New York 
Department of Environmental Conservation; Richard Eskin, Maryland Department 
of the Environment; Carlton Haywood, Interstate Commission on the Potomac River 
Basin; David Heicher, Susquehanna River Basin Commission; Ruth Izraeli, United 
States Environmental Protection Agency; James Keating, United States Environmental 
Protection Agency; Teresa Koon, West Virginia Department of Environmental 
Protection; Robert Koroncai, United States Environmental Protection Agency; Bruce 
Michael, Maryland Department of Natural Resources; Matt Monroe, West Virginia 
Department of Agriculture; Kenn Pattison, Pennsylvania Department of 
Environmental Protection; Russ Perkinson, Virginia Department of Conservation and 
Recreation; Alan Pollock, Virginia Department of Environmental Quality; John 
Schneider, Delaware Department of Natural Resources and Environmental Control; 
Ann Swanson, Chesapeake Bay Commission; Robert Yowell, Pennsylvania 
Department of Environmental Protection. 

The individual and collective contributions from members of the Chesapeake Bay 
Program Office are also acknowledged: Holly Davis, University of Maryland Center 
for Environmental Science, Howard Weinberg, University of Maryland Center for 
Environmental Science/Chesapeake Bay Program Office; John Wolf, National Park 
Service, Jacob Goodwin, Chesapeake Research Consortium/Chesapeake Bay Program 
Office; Jamie McNees, Chesapeake Research Consortium/Chesapeake Bay Program 
Office. 


Acknowledgments 


1 


cha pter 

Introduction 


Since the signing of the multijurisdicational Chesapeake 2000 agreement, the U.S. 
Environmental Protection Agency (EPA), in cooperation with its six watershed State 
partners and the District of Columbia, has developed a series of water quality criteria 
guidance documents in accordance with Section 117b of the Clean Water Act. 
Chesapeake Bay regional water quality criteria were developed and adopted into 
state water quality standards regulations protective of living resources and their habi¬ 
tats. Five aquatic life tidal-water designated uses were defined by the partners (U.S. 
EPA 2003a) apportioning the Chesapeake Bay and its tidal tributaries into appro¬ 
priate habitats: 

• Migratory fish spawning and nursery habitat; 

• Open water fish and shellfish habitat; 

• Deep-water seasonal fish and shellfish habitat; 

• Deep-channel seasonal refuge habitat; and 

• Shallow-water Bay grass habitat 

Ambient Water Quality Criteria for Dissolved Oxygen, Water Clarity and Chloro¬ 
phyll a for the Chesapeake Bay and Its Tidal Tributaries (Regional Criteria 
Guidance) April 2003 has been the foundation document defining Chesapeake Bay 
water quality criteria and recommended implementation procedures for monitoring 
and assessment (U.S. EPA 2003a). The Technical Support Document for Identifica¬ 
tion of Chesapeake Bay Designated Uses and Attainability October 2003 defined the 
five tidal water designated uses to be protected through the published Bay water 
quality criteria (U.S. EPA 2003b). Six addendum documents have been published 
since April 2003 addressing detailed issues involving further delineation of tidal 
water designated uses (U.S. EPA 2004a), Chesapeake Bay Program analytical 
segmentation schemes (U.S. EPA 2004c, 2005), detailed criteria attainment and 
assessment procedures, (U.S. EPA 2004b, 2007a), and Chesapeake Bay numerical 
chlorophyll a criteria (2007b). 

The detailed procedures are assessing attainment of the Chesapeake Bay water 
quality criteria advanced through the collective EPA, States and District of Columbia 
partner efforts to develop and apply procedures that incorporate, at the most 
advanced state, magnitude, frequency, duration, space and time considerations with 


chapter i 


Introduction 


2 


biologically-based reference conditions and cumulative frequency distributions. As 
a rule, the best test of any new method or procedure is putting it to work with stake¬ 
holder involvement. Through the work of its Criteria Assessment Protocols 
Workgroup, the Chesapeake Bay Program has an established forum for resolving 
details of baywide criteria assessment procedure development and implementation. 
This addendum document provides previously undocumented features of the present 
procedures and refinements and clarifications to the previously published Chesa¬ 
peake Bay water quality criteria assessment procedures. 

Chapter 2 documents the most recent Chesapeake Bay 92-segment scheme used for 
criteria assessment. 

Chapter 3 documents refinements and additions to the procedures for assessing the 
previously published Chesapeake Bay dissolved oxygen criteria. 

Chapter 4 documents refinements and additions to the procedures for assessing the 
previously published Chesapeake Bay water clarity and SAV criteria and deter¬ 
mining attainment of the shallow-water designated use. 

Chapter 5 documents refinements and additions to the procedures for assessing the 
previously published Chesapeake Bay chlorophyll a criteria. 

Appendices to the chapters include more detailed documentation on derivation of the 
criteria assessment procedure elements and step-by-step through procedures for 
assessing criteria. 

This document represents the fifth formal addendum to the 2003 Chesapeake Bay 
water quality criteria document; as such readers should regard the sections in this 
document as new or replacement chapters and appendices to the original published 
report. The criteria assessment procedures published in this addendum also replace 
and otherwise supersede similar criteria assessment procedures originally published 
in the 2003 Regional Criteria Guidance and the 2004 and 2007 addenda (U.S. EPA 
2003a, 2004a, 2007a, b). Publication of future addendums by EPA on behalf of the 
Chesapeake Bay Program watershed jurisdictional partners is likely as continued 
scientific research and management applications reveal new insights and knowledge 
that should be incorporated into revisions of state water quality standards regulations 
in upcoming triennial reviews. 


LITERATURE CITED 

U.S. Environmental Protection Agency. 2003a. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries 
(Regional Criteria Guidance) April 2003. EPA 903-R-03-002. Region III Chesapeake Bay 
Program Office, Annapolis. MD. 

U.S. Environmental Protection Agency. 2003b. Technical Support Document for Identifica¬ 
tion of Chesapeake Bay Designated Uses and Attainability. October 2003. Region III 
Chesapeake Bay Program Office. EPA 903-R-03-004. Annapolis, MD. 


chapter i 


Introduction 



U.S. Environmental Protection Agency. 2004a. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll afar the Chesapeake Bay and Its Tidal Tributaries - 
2004 Addendum. EPA 903-R-04-005. Region III Chesapeake Bay Program Office, 
Annapolis, MD. 

U.S. Environmental Protection Agency. 2004b. Technical Support Document far Identifica¬ 
tion of Chesapeake Bay Designated Uses and Attainability - 2004 Addendum. October 2004. 
Region III Chesapeake Bay Program Office. EPA 903-R-04-006. Annapolis. MD. 

U.S. Environmental Protection Agency. 2004c. Chesapeake Bay Program Analytical 
Segmentation Scheme: Revisions, Decisions and Rationales 1983-2003. October 2004. 
Region III Chesapeake Bay Program Office, Annapolis, MD. EPA 903-R-04-008. 

U.S. Environmental Protection Agency. 2005. Chesapeake Bay Program Analytical Segmen¬ 
tation Scheme: Revisions, Decisions and Rationales 1983-2003. 2005 Addendum. December 
2005. Region III Chesapeake Bay Program Office, Annapolis, MD. EPA 903-R-05-004. 

U.S. Environmental Protection Agency. 2007a. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll afar the Chesapeake Bay and Its Tidal Tributaries - 
2007 Addendum. July 2007. EPA 903-R-07-003. Region III Chesapeake Bay Program Office, 
Annapolis, MD. 

U.S. Environmental Protection Agency. 2007b. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries 
-Chlorophyll a Addendum. October 2007. EPA 903-R-07-005. Region III Chesapeake Bay 
Program Office, Annapolis, MD. 


chapter iii 


Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria 







































cha pter 

2008 92-Segment Scheme 
for the Chesapeake Bay 
Water Quality Criteria 


BACKGROUND 

For 25 years, the Chesapeake Bay Program partners have used various versions of a 
basic segmentation scheme to organize the collection, analysis and presentation of 
environmental data. The Chesapeake Bay Program Segmentation Scheme: Revi¬ 
sions, decisions and rationales provided documentation on the spatial segmentation 
scheme of the Chesapeake Bay and its tidal tributaries and the later revisions and 
changes over the last 25 years (U.S. EPA 2004b, 2005). This chapter provides 
concise information on the historical 1983, 1997, 2003 segmentation schemes and 
illustrates the recommended 2008 92-segment scheme for assessing Chesapeake Bay 
water quality criteria. 


CHESAPEAKE BAY PROGRAM 
SEGMENTATION SCHEMES 

Segmentation is the compartmentalization of the estuary into subunits based on 
selected criteria. The Chesapeake Bay ecosystem is diverse and complex, and the 
physical and chemical factors which vary throughout the Bay determine the biolog¬ 
ical communities and affect the kind and extent of their response to pollution stress. 
These same factors also influence their response to restoration and remediation. For 
diagnosing anthropogenic impacts, segmentation is a way to group regions having 
similar natural characteristics so that differences in water quality and biological 
communities among similar segments can be identified and their source elucidated. 
For management purposes, segmentation is a way to group similar regions to define 
a range of water quality and resource objectives, target implementation of specific 
actions and monitor responses. It provides a meaningful way to summarize and 


chapter ii 


2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria 





6 


present information in parallel with these objectives and it is a useful geographic 
pointer for data management. 

The Chesapeake Bay Program Segmentation Scheme: Revisions, decisions and 
rationales 1983-2003 (U.S. EPA 2004b, 2005) contains the following maps and 
tables used to document changes to the segmentation scheme from 1983 through 
2003 as well as provide the jurisdictions with detailed documentation on the 
geographical delineation of each segment’s boundaries: 

• Maps for the 1983, 1997 and 2003 segmentation schemes; 

• Statistics on the perimeter, surface area and volume of each Chesapeake Bay 
Program segment; 

• Narrative descriptions of each of the coordinates bounding each Chesapeake 
Bay Program segment; and 

• Maps of all the Chesapeake Bay Water Quality Monitoring Program stations 
displayed by segment by Maryland, Virginia and the District of Columbia. 

A concise history of the original 1983 segmentation scheme, and the 1997 and 2003 
revised segmentation schemes is published in Chapter 3 of the U.S. EPA (2004a) 
Technical Support Document for identification of Chesapeake Designated Uses and 
Attainability, 2004 Addendum. A detailed history of segmentation schemes is 
provided in the Chesapeake Bay Program Segment Scheme document at 
http://www.chesapeakebay.net/pubs/segmentscheme.pdf and the summary docu¬ 
ments of U.S. EPA 2004b, 2005. 


2008 CHESAPEAKE BAY AND TIDAL TRIBUTARIES 

92-SEGMENT SCHEME 

The 92-segment scheme for the Chesapeake Bay and its tidal tributaries used for 
dissolved oxygen and water clarity assessments in the 2008 303d/305b listing efforts 
of the four Bay tidal jurisdictions is documented here. The 92-segment scheme was 
derived from: 1) the 2003 published 78-segment scheme with the addition of juris¬ 
dictional boundary lines imposed to create 89 segments; then 2) includes only the 
split segments agreed upon for the tidal James and Potomac rivers. The result of the 
State partners’ decisions on the Chesapeake Bay water quality criteria assessment 
framework is the 92-segment scheme (Figure II-1), a subset of the 2003 104-segment 
scheme that defined boundaries of split segments published in U.S. EPA 2004b. 
Table II-1 is a complementary reference table that lists the 92 segments definitions 
according to their application across the 25 year history of Chesapeake Bay segment 
schemes. 


chapter ii 


2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria 




7 




ington 

RHDWH 
i i WSTMfT 
AKATF MO f 
AKMTF DC J 


-NANOH 


Chesapeake Bay 303d list segment 


NORTF ELK OH 

C1DOH MO 


Baltimore 


C8i 

BSMOH 


C*DOH_DE 

BOHOH 


GUNOH 
MIDOH J* 


SA : :■ h 


rum 


unsTr 


scvmh 

SOUMH 


Wash in 


POTTFDC 
POTTF MO 


OC - 




=>C~' c VA 


POTOH 1 MO 


NAN * M3 


KJIW M 


Salisbury 


nPFTF 


P 3: IF 


POCOH HO 


POCCH #1 


POCfcH_M3 
POCMhVfc 
TANMH VA 


=> >.*• - 


Richmond 


JMSTF 2 




MSOn 


- LYNPH 

N orfolk 


LA - 1*1 


ELS'H 


0 12.5 2£ 

I I I I I I 


— EocMri 


S3 M3r<fcn 


Figure 11-1. 2008 Chesapeake Bay 92-segment scheme. 


chapter ii 


2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria 






























8 


Table 11-1. Segment acronyms and their historical context to 1983, 1997, 2003 and 2008 Chesapeake Bay 
segmentation schemes 1 . 


Chesapeake 
Bay Program 
Segment-Name 
Nomenclature 1 

Chesapeake Bay Program Segment Scheme Membership 

(Y=Yes, N=No) 

Tidal Water Body 

1985 

78 segments 

1997 

89 segments 

2003 

104 segments 

2008 

92 segments 

ANATF 

Y 

N 

N 

N 

Anacostia River 

ANATF DC 

N 

Y 

Y 

Y 

Anacostia River. DC 

ANATF MD 

N 

Y 

Y 

Y 

Anacostia River, MD 

APPTF 

Y 

Y 

Y 

Y 

Appomattox River 

BACOH 

Y 

Y 

Y 

Y 

Back River 

B1GMH 

Y 

Y 

N 

Y 

Big Annemessex River 

BIGMH1 

N 

N 

Y 

N 

Big Annemessex River, Lower 

B1GMH2 

N 

N 

Y 

N 

Big Annemessex River. Upper 

BOHOH 

Y 

Y 

Y 

Y 

Bohemia River 

BSHOH 

Y 

Y 

Y 

Y 

Bush River 

C&DOH 

Y 

N 

N 

N 

C&D Canal 

C&DOH_DE 

N 

Y 

Y 

Y 

C&D Canal, DE 

C&DOH MD 

N 

Y 

Y 

Y 

C&D Canal, MD 

CB1TF 

Y 

Y 

N 

Y 

Northern Chesapeake Bay 

CB1TF1 

N 

N 

Y 

N 

Northern Chesapeake Bay - 
Turkey Pt South 

CB1TF2 

N 

N 

Y 

N 

Northern Chesapeake Bay - 
Susquehanna River and Flats 

CB20H 

Y 

Y 

Y 

Y 

Upper Chesapeake Bay 

CB3MH 

Y 

Y 

Y 

Y 

Upper Central Chesapeake Bay 

CB4MH 

Y 

Y 

Y 

Y 

Middle Central Chesapeake Bay 

CB5MH 

Y 

N 

N 

N 

Lower Central Chesapeake Bay 

CB5MH_MD 

N 

Y 

Y 

Y 

Lower Central Chesapeake Bay, 
MD 

CB5MH_VA 

N 

Y 

Y 

Y 

Lower Central Chesapeake Bay, 

VA 

CB6PH 

Y 

Y 

Y 

Y 

Western Lower Chesapeake Bay 

CB7PH 

Y 

Y 

Y 

Y 

Eastern Lower Chesapeake Bay 

CB8PH 

Y 

Y 

Y 

Y 

Mouth of Chesapeake Bay 

CHKOH 

Y 

Y 

Y 

Y 

Chickahominy River 

CHOMH1 

Y 

Y 

Y 

Y 

Lower Choptank River 


'Note: Group acronyms are a combination of river and salinity zone membership. An example is BSHOH where BSH=Bush River and 
OH=01igohaline zone. Salinity zones are TF=Tidal Fresh, OH=01igohaline, MH=Mesohaline, PH=Polyhaline. 


chapter ii 


2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria 


































Table 11-1. (continued). 


Chesapeake 
Bay Program 
Segment-Name 
Nomenclature 1 

Chesapeake Bay Program Segment Scheme Membership 
(Y=Yes, N=No) 

Tidal Water Body 

1985 

78 segments 

1997 

89 segments 

2003 

104 segments 

2008 

92 segments 

CHOMH2 

Y 

Y 

Y 

Y 

Mouth of Choptank River 

CHOOH 

Y 

Y 

Y 

Y 

Middle Choptank River 

CHOTF 

Y 

Y 

Y 

Y 

Upper Choptank River 

CHSMH 

Y 

Y 

Y 

Y 

Lower Chester River 

CHSOH 

Y 

Y 

Y 

Y 

Middle Chester River 

CHSTF 

Y 

Y 

Y 

Y 

Upper Chester River 

CRRMH 

Y 

Y 

Y 

Y 

Corrotoman River 

EASMH 

Y 

Y 

Y 

Y 

Eastern Bay 

EBEMH 

Y 

Y 

Y 

Y 

Eastern Branch Elizabeth River 

ELIPH 

Y 

Y 

Y 

Y 

Mouth-mid Elizabeth River 

ELKOH 

Y 

Y 

N 

Y 

Elk River 

ELKOH1 

N 

N 

Y 

N 

Elk River, Upper 

ELKOH2 

N 

N 

Y 

N 

Elk River, Lower 

FSBMH 

Y 

Y 

Y 

Y 

Fishing Bay 

GUNOH 

Y 

Y 

N 

Y 

Gunpowder River 

GUNOH1 

N 

N 

Y 

N 

Gunpowder River, Upper 

GUNOH2 

N 

N 

Y 

N 

Gunpowder River, Lower 

HNGMH 

Y 

Y 

Y 

Y 

Honga River 

JMSMH 

Y 

Y 

Y 

Y 

Lower James River 

JMSOH 

Y 

Y 

Y 

Y 

Middle James River 

JMSPH 

Y 

Y 

Y 

Y 

Mouth of James River 

JMSTF 

Y 

Y 

N 

N 

Upper James River 

JMSTF1 

N 

N 

Y 

Y 

Upper James River - Lower 

JMSTF2 

N 

N 

Y 

Y 

Upper James River - Upper 

LAFMH 

Y 

Y 

Y 

Y 

Lafayette River 

LCHMH 

Y 

Y 

Y 

Y 

Little Choptank River 

LYNPH 

Y 

Y 

Y 

Y 

Lynnhaven River 

MAGMH 

Y 

Y 

Y 

Y 

Magothy River 

MANMH 

Y 

Y 

N 

Y 

Manokin River 

MANMH1 

N 

N 

Y 

N 

Manokin River, Lower 

MANMH2 

N 

N 

Y 

N 

Manokin River, Upper 

MATTF 

Y 

Y 

Y 

Y 

Mattawoman Creek 


chapter ii 


2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria 






































10 


Table 11-1. (continued). 


Chesapeake 
Bay Program 
Segment-Name 
Nomenclature 1 

Chesapeake Bav Program Segment Scheme Membership 

(Y=Yes, N=No) 

Tidal Water Body 

1985 

78 segments 

1997 

89 segments 

2003 

104 segments 

2008 

92 segments 

MIDOH 

Y 

Y 

Y 

Y 

Middle River 

MOBPH 

Y 

Y 

Y 

Y 

Mobjack Bay 

MPNOH 

Y 

Y 

Y 

Y 

Lower Mattaponi River 

MPNTF 

Y 

Y 

Y 

Y 

Upper Mattaponi River 

NANMH 

Y 

Y 

Y 

Y 

Lower Nanticoke River 

NANOH 

Y 

Y 

Y 

Y 

Middle Nanticoke River 

NANTF 

Y 

N 

N 

N 

Upper Nanticoke River 

NANTF DE 

N 

Y 

Y 

Y 

Upper Nanticoke River, DE 

NANTF MD 

N 

Y 

Y 

Y 

Upper Nanticoke River, MD 

NORTF 

Y 

Y 

Y 

Y 

Northeast River 

PATMH 

Y 

Y 

Y 

Y 

Patapsco River 

PAXMH 

Y 

Y 

N 

Y 

Lower Patuxent River 

PAXMH1 

N 

N 

Y 

N 

Lower Patuxent River, Lower 

PAXMH2 

N 

N 

Y 

N 

Lower Patuxent River, Upper 

PAXMH 3 

N 

N 

Y 

N 

Lower Patuxent River, Mill Creek 

PAXMH4 

N 

N 

Y 

N 

Lower Patuxent River, 

Cuckold Creek 

PAXMH 5 

N 

N 

Y 

N 

Lower Patuxent River, 

St. Leonard Creek 

PAXMH6 

N 

N 

Y 

N 

Lower Patuxent River, Island Creek 

PAXOH 

Y 

Y 

Y 

Y 

Middle Patuxent River 

PAXTF 

Y 

Y 

Y 

Y 

Upper Patuxent River 

PIAMH 

Y 

Y 

Y 

Y 

Piankatank River 

PISTF 

Y 

Y 

Y 

Y 

Piscataway Creek 

PMKOH 

Y 

Y 

Y 

Y 

Lower Pamunkey River 

PMKTF 

Y 

Y 

Y 

Y 

Upper Pamunkey River 

POCMH 

Y 

N 

N 

N 

Lower Pocomoke River 

POCMH_MD 

N 

Y 

Y 

Y 

Lower Pocomoke River, MD 

POCMH_VA 

N 

Y 

Y 

Y 

Lower Pocomoke River, VA 

POCOH 

Y 

N 

N 

N 

Middle Pocomoke River 

POCOH MD 

N 

Y 

Y 

Y 

Middle Pocomoke River, MD 

POCOH_VA 

N 

Y 

Y 

Y 

Middle Pocomoke River, VA 

POCTF 

Y 

Y 

Y 

Y 

Upper Pocomoke River 

POTMH 

Y 

N 

N 

N 

Lower Potomac River 

POTMH_MD 

N 

Y 

Y 

Y 

Lower Potomac River, MD 


chapter ii 


2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria 






































11 


Table 11-1. (continued). 


Chesapeake 
Bay Program 
Segment-Name 
Nomenclature 1 

Chesapeake Bay Program Segment Scheme Membership 

(Y=Yes, N=No) 


1985 

78 segments 

1997 

89 segments 

2003 

104 segments 

2008 

92 segments 

Tidal Water Body 

POTMH VA 

N 

Y 

Y 

Y 

Lower Potomac River, VA 

POTOH 

Y 

N 

N 

N 

Middle Potomac River 

POTOH MD 

N 

Y 

N 

N 

Middle Potomac River, MD 

POTOH VA 

N 

Y 

Y 

Y 

Middle Potomac River, VA 

POTOH 1_MD 

N 

N 

Y 

Y 

Middle Potomac River, 

MD Mainstem 

POTOH 2_MD 

N 

N 

Y 

Y 

Middle Potomac River, 

MD Port Tobacco River 

POTOH 3_MD 

N 

N 

Y 

Y 

Middle Potomac River, 

MD Nanjemoy Creek 

POTTF 

Y 

N 

N 

N 

Upper Potomac River 

POTTF DC 

N 

Y 

Y 

Y 

Upper Potomac River, DC 

POTTF_MD 

N 

Y 

Y 

Y 

Upper Potomac River. MD 

POTTF_VA 

N 

Y 

Y 

Y 

Upper Potomac River, VA 

RHDMH 

Y 

Y 

Y 

Y 

Rhode River 

RPPMH 

Y 

Y 

Y 

Y 

Lower Rappahannock River 

RPPOH 

Y 

Y 

Y 

Y 

Middle Rappahannock River 

RPPTF 

Y 

Y 

Y 

Y 

Upper Rappahannock River 

SASOH 

Y 

Y 

N 

Y 

Sassafras River 

SASOH1 

N 

N 

Y 

N 

Sassafras River, Lower 

SASOH2 

N 

N 

Y 

N 

Sassafras River. Upper 

SBEMH 

Y 

Y 

Y 

Y 

Southern Branch Elizabeth River 

SEVMH 

Y 

Y 

Y 

Y 

Severn River 

SOUMH 

Y 

Y 

Y 

Y 

South River 

TANMH 

Y 

N 

N 

N 

Tangier Sound 

TANMH MD 

N 

Y 

N 

Y 

Tangier Sound. MD 

TANMH VA 

N 

Y 

Y 

Y 

Tangier Sound. VA 

TANMH 1 MD 

N 

N 

Y 

N 

Tangier Sound. MD. Main Body 

TANMH2_MD 

N 

N 

Y 

N 

Tangier Sound. MD. Deal Island to 
Mouth of Nanticoke River 

WBEMH 

Y 

Y 

Y 

Y 

Western Branch Elizabeth River 

WBRTF 

Y 

Y 

Y 

Y 

Western Branch Patuxent River 

W1CMH 

Y 

Y 

Y 

Y 

Wicomico River 

WSTMH 

Y 

Y 

Y 

Y 

West River 

YRKMH 

Y 

Y 

Y 

Y 

Middle York River 

YRKPH 

Y 

Y 

Y 

Y 

Lower York River 


chapter ii • 2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria 







































12 


UNRESOLVED BOUNDARY FOR DISTRICT 
OF COLUMBIA UPPER POTOMAC RIVER 

This 92-segment scheme is the agreed upon 2008 assessment segmentation. Final 
programming adjustments for boundary conditions of the jurisdictions were made in 
autumn 2007. During early winter 2007/8, an unresolved upper boundary location 
for the District of Columbia segment of the Tidal Fresh Potomac River came to light 
due to unresolved station classifications (tidal vs. nontidal) to revise the boundary. 
With assessment calculations underway, it was a nontrivial task to revise the map at 
this segment boundary which could have affected assessments already completed for 
the jurisdictions. The result, coupled with data limitations affected Washington 
District of Columbia in 2008 for a “no attainment assessment” result in their 
303d/305b listing. This boundary condition will be resolved for the next triennial 
review. 


LITERATURE CITED 

U.S. Environmental Protection Agency. 2003a. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries 
(Regional Criteria Guidance) April 2003. EPA 903-R-03-002. Region III Chesapeake Bay 
Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2003b. Technical Support Document for Identifica¬ 
tion of Chesapeake Bay Designated Uses and Attainability. October 2003. October 2004. 
Region III Chesapeake Bay Program Office. EPA 903-R-03-004. Annapolis, MD. 

U.S. Environmental Protection Agency. 2004a. Technical Support Document for Identifica¬ 
tion of Chesapeake Bay Designated Uses and Attainability - 2004 Addendum. October 2004. 
Region III Chesapeake Bay Program Office. EPA 903-R-04-006. Annapolis. MD. 

U.S. Environmental Protection Agency. 2004b. Chesapeake Bay Program Analytical 
Segmentation Scheme: Revisions, Decisions and Rationales 1983-2003. October 2004. 
Region III Chesapeake Bay Program Office, Annapolis, MD. EPA 903-R-04-008. 

U.S. Environmental Protection Agency. 2005. Chesapeake Bay Program Analytical Segmen¬ 
tation Scheme: Revisions, Decisions and Rationales 1983-2003. 2005 Addendum. December 
2005. Region III Chesapeake Bay Program Office, Annapolis. MD. EPA 903-R-05-004. 


chapter ii 


2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria 




cha pter ill 


Refinements to Procedures 
for Assessing Chesapeake Bay 
Dissolved Oxygen Criteria 


BACKGROUND 

In 2003, the EPA published detailed criteria for dissolved oxygen tailored to 
different habitats within the Chesapeake Bay and its tidal tributaries (U.S. EPA 
2003a). Oxygen is critical to most forms of life in the Bay; it must be available in 
adequate concentrations to support overall ecosystem health. Minimum concentra¬ 
tions of dissolved oxygen must be present to support the diversity of species and 
their various life stages requiring protection. 

Dissolved oxygen criteria were established for Chesapeake Bay that varied in space 
(e.g., designated uses) and time (e.g., summer) to provide protection for different 
species and communities. The criteria were also designed around several durations 
(e.g., 30-day, 1-day) to reflect the varying oxygen tolerances for different life stages 
(e.g., larval, juvenile, adult) and effects (e.g., mortality, growth, behavior). Thus, the 
dissolved oxygen criteria include multiple components. Each component includes a 
target of dissolved oxygen concentration, the duration over which the concentration 
is averaged, the space (designated-use area) where the criterion applies, and a time 
(season, months) when the criterion applies. EPA has published, and the States 
adopted into their water quality standards regulations, dissolved oxygen criteria 
protective of migratory spawning, open-water, deep-water, and deep-channel desig- 
nated-use habitats (U.S. EPA 2003a). These dissolved oxygen criteria include 
30-day, 7-day, and 1-day means along with instantaneous minima. 

Since the Chesapeake Bay dissolved oxygen criteria were published in 2003, the 
capability of fully assessing all the dissolved oxygen criteria for all four designated 
uses over all applicable time periods has progressed, however, some limitations 
remain. The refined and expanded dissolved oxygen criteria assessment methodolo¬ 
gies documented in this chapter replace the methodologies previous published by 
EPA. Work by EPA and its partners will continue to refine these methodologies to 
reduce uncertainty further and to increase confidence in the resulting assessments. 


chapter iii 


Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria 





Developing, validating and publishing EPA-recommended methodologies for 
assessing the full array of Chesapeake Bay dissolved oxygen criteria duration 
components will also prove critical. In this chapter and its associated appendices, 
details and clarifications regarding data structure and assessment protocols are 
provided for completing Chesapeake Bay dissolved oxygen criteria attainment 
computations. 


DISSOLVED OXYGEN ASSESSMENT: 

STATIONS AND ACCEPTED DATA 

The EPA water quality criteria assessment methodologies adopted by the Chesa¬ 
peake Bay watershed jurisdictions recommend 3 consecutive years of data to 
construct the cumulative frequency distribution function to compare with the biolog¬ 
ical or other recommended reference curve (U.S. EPA 2003a). Step-by-step 
procedures of the Chesapeake Bay dissolved oxygen criteria attainment assessment 
methodology are provided for in Appendix A. A dissolved oxygen dataset was 
developed for a suite of Chesapeake Bay Program monitoring stations, and ancillary 
monitoring stations (VA), in the tidal waters of the Chesapeake Bay and its tidal trib¬ 
utaries and embayments (Appendix B) stored on-line in the Chesapeake Information 
Management System (CIMS). 

A database table was assembled for dissolved oxygen (pg/L), water temperature (°C) 
and salinity (ppt) using all tidal Chesapeake Bay Program Water Quality Monitoring 
stations from CIMS. The stations are a composite of Maryland and Virginia’s fixed 
station water quality monitoring network and the calibration and swapout data (i.e., 
swap out data is data collected when in situ water quality monitoring meters are 
switched for maintenance) from their shallow-water monitoring programs (i.e., contin¬ 
uous monitoring and DATAFLOW 1 spatially intensive monitoring). The Chesapeake 
Bay Program supported monitoring data is relatively extensive in time with a 23-year 
history, however, the temporal density of the fixed station network is biweekly to 
monthly and spatial distribution of stations is not particularly dense to meet all Chesa¬ 
peake Bay water quality criteria assessment needs. Therefore, ancillary data of 
sufficient quality is desirable and recommended for use when available to enhance the 
attainment assessments, especially where CBP data are limited or lacking. 

Ancillary data derived outside of the Chesapeake Bay Program supported water 
quality monitoring program that were considered to have sufficient quality, passing 
rigorous quality assurance/quality control standards, were added to the CIMS data. 
Examples of additional water quality monitoring data were those data provided by 


'DATAFLOW: A field sampling technology used on a boat while a watercraft is underway that collects 
spatially intensive data (hence DATA) for five environmental parameters (water temperature, salinity, 
dissolved oxygen, turbidity (ntu), and fluorescence (used to estimate chlorophyll a) collected from a 
How-through (hence FLOW) stream of water collected near the surface of the water column. The 
following website provides additional details about DATAFLOW and water quality monitoring with 
DATAFLOW: http://mddnr.chesapeakebay.net/sim/index.cfm . 


chapter iii 


Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria 







Virginia authorities for the 2008 303d/305b listing analyses that were collected from 
the Virginia Chesapeake Bay benthic monitoring program and the Alliance for the 
Chesapeake Bay’s (ACB) Virginia volunteer monitoring program. 


PYCNOCLINE DEFINITION AND BOUNDARIES 

REVISING DESIGNATED USES BOUNDARIES WITH ENHANCED 
PYCNOCLINE DEFINITION PROCEDURE 

In U.S. EPA (2003a) Ambient Water Quality Criteria for Dissolved Oxygen , Water 
Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries (Regional 
Criteria Guidance), EPA identified five habitats (or designated uses) providing a 
context for adequately protective Chesapeake Bay water quality criteria. Water 
quality criteria and assessment procedures were developed for dissolved oxygen, 
water clarity and chlorophyll a, published (U.S. EPA 2003a, 2004a,b, 2007a,b), and 
have progressively been adopted into State water quality standards regulations. The 
five designated uses were 1) migratory fish spawning and nursery designated use, 2) 
shallow-water bay grass designated use, 3) open-water fish and shellfish designated 
use, 4) deep-water seasonal fish and shellfish designated use and 5) deep-channel 
seasonal refuge designated use (U.S. EPA 2003b). EPA published Technical Support 
Document for Identification of Chesapeake Bay Designated Uses and Attainability 
(U.S. EPA 2003b, 2004b) which provided further information on the development 
and geographical extent of the designated uses to which the criteria may apply. 
Refinements to boundary definitions involving open water, deep water and deep 
channel have been developed, as described below, to standardize layer definitions. 

CALCULATION OF UPPER AND LOWER PYCNOCLINES FOR 
DISSOLVED OXYGEN DESIGNATED USE CRITERIA ASSESSMENT 

Vertical stratification is foremost among the physical factors affecting dissolved 
oxygen concentrations in some parts of Chesapeake Bay and its tidal tributaries. For 
the purposes of water quality criteria attainment assessment, three layers are defined 
for designated use assessments: 1) an upper mixed layer above the upper pycnocline 
boundary; 2) deep water layer constrained by the upper and lower pycnocline bound¬ 
aries; and 3) the lower mixed layer below the lower pycnocline boundary (U.S. EPA 
2003a, 2003b). The depths of the upper and lower mixed layers are used to deter¬ 
mine designated use boundaries for the dissolved oxygen assessment. In segments 
where deep water and deep channel habitats are applicable, deep channel is defined 
as the lower mixed layer, open water is defined as the upper mixed layer, and deep 
water is the interpycnocline layer between the upper and lower mixed layers. 

Temperature (°C) and salinity (ppt) are used to calculate density which, in turn, is used 
to calculate pycnocline boundaries. Density is calculated using the method described in: 

Algorithms for computation of fundamental properties of seawater. Endorsed 
by UNESCO/SCOR/ICES/IAPSO Joint Panel on Oceanographic Tables and 
Standards and SCOR Working Group 51. Fofonoff, N P; Millard, R C Jr. 
UNESCO technical papers in marine science. Paris , no. 44, pp. 53. 1983. 


chapter iii 


Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria 



16 


For each vertical column of temperature and salinity data throughout the water 
column, the existence of the upper and lower pycnocline boundaries are determined 
by looking for the shallowest robust vertical change in density greater than 0.1 
kg/mVm for the upper boundary and deepest change of greater than 0.2 kg/m Vm for 
the lower boundary. To be considered robust, the density gradient must not reverse 
direction at the next measurement and must be accompanied by a change in salinity 
and temperature. 

Upper and lower pycnocline boundaries, where present, are interpolated in two 
dimensions. The depth to the upper pycnocline boundary tends to be stable across 
horizontal space in the estuary and so spatial definition of that boundary using inter¬ 
polation generally works well. However, interpolation of the lower boundary is more 
complicated because the results can conflict with 1) the upper boundary definition or 
2) with the actual bathymetry of the Bay. As a result, interpolation of the lower 
boundary should be performed based on “fraction of water column depth”. 

In the computations, the lower pycnocline is actually stored as “fraction of water 
column below lower pycnocline,” and calculated by dividing the lower pycnocline 
depth by the total depth and subtracting the product from 1 as follows: 

Example: Lower pycnocline depth = 10 m 

Total depth = 15 m 

% of total depth below lower pycnocline = 1 -(10/15) = -.333 or about 33%. 

When counting violations, the measures are converted back into an actual depth 
before comparing measurements to it. To locate the lower pycnocline, multiply the 
total depth at the given measurement location for that day by (1- %below lower 
pycnocline), in this example it is 15(1-.33) = 10.01. 

This calculation produces essentially the same depth of lower pycnocline. It is 
important to proceed in this approach since total depth measurements may differ 
across sampling dates. By following this procedure for working with the lower pycn¬ 
ocline calculation it avoids the case where you could have a lower pycnocline value 
below the total depth. If no lower boundary is detected then the fraction is zero. 

The standardized method for calculating upper and lower boundaries of the pycno¬ 
cline uses water column measurements of water temperature and salinity. Ambient 
Water Quality Criteria for Dissolved Oxygen , Water Clarity and Chlorophyll a for 
Chesapeake Bay and its Tributaries - 2004 Addendum (U.S. EPA 2004a) provided 
two basic rules for determination of pycnocline depth: 

1. From the water surface downward, the first density slope observation that is 
greater than 0.1 kg/m 3 /m is designated as the upper pycnocline boundary 
provided that: 

a. That observation is not the first observation in the water column and 

b. The next density slope observation is positive. 

2. From the bottom sediment-water interface upward, the first density slope 
observation that is greater than 0.2 kg/mVm is designated as the lower pycno¬ 
cline depth provided that: 

a. An upper pycnocline depth exists; 


chapter iii 


Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria 


17 


b. There is a bottom mixed layer, defined by the first or second density slope 
observation from the bottom sediment-water interface being less than 0.2 
kg/m 3 /m 

c. The next density slope observation is positive. 

U.S. EPA (2004a, see pg. 87) also provided the procedure for calculation of the 
vertical density profile. 

These two decision rules remain unchanged. The detailed step-by-step procedure for 
applying the two decision rules has been provided here. 

Determining the vertical density gradient and defining pycnocline depths requires a 
vertical profile of salinity and water temperature measurements collected at multiple 
depths and computed as follows: 

1. Sort the vertical profile of data from the water surface downwards through the 
water column. 

2. For each depth at which there are measurements, calculate a water density 
value as crT, or “sigma T”, using water temperature and salinity measurements 
for that depth. Use the following method and equations: 

crT = a(T) + b(T)*S, where: 

T = temperature (°C) 

S = salinity (ppt) 

a and b are polynomial functions of T 

a(T) = -9.22x10 3 + 5.59x10 2 * T- 7.88x1 O' 3 * T 2 + 4.18xl0' 5 * T 3 
b(T) = 8.04x10-' - 2.92x10 3 * T + 3.12x10 5 * T 2 

3. Look down through the profile. Wherever the difference between sequential 
depth measurements is < 0.19 meters, average the two depth measurements and 
their corresponding salinity and density measurements. 

4. Look down through the profile again. If there are still any depths (depth, 
salinity, temperature and density measurements) <0.19 meters apart, then 
average them again. Continue until there are no depths <0.19 meters apart. 

5. Starting at the surface measurement and continuing until the deepest measure¬ 
ment in the profile, calculate the change in salinity and density between each 
sampling depth. For example, for two density values at 1 meter depth (y]) and 
2 meters depth (y 2 ) respectively, the change in density, or AcrT = y 2 -yj. Like¬ 
wise, for salinity measurements AS = y 2 -yj. 

6. Assign a depth measurement to each pair of A values (AS, AcrT) equal to the 
average of two depths x 2 and x, used to calculate the A values. Thus for the two 
measurements y 2 and y,, calculate the accompanying depth as (xj + x 2 )/2. You 
should now have a vertical profile of AS and AcrT values with an accom¬ 
panying depth. 

7. To find the upper boundary of the pycnocline, look at the vertical profile of 
AcrT, beginning with the second value (from the surface) and excluding the two 
deepest values: 

a. IF AcrT >0.1, 

b. AND IF AcrT for the next depth is greater than zero. 


chapter iii 


Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria 


18 


c. AND IF AS >0.1, 

d. Then this depth represents the upper boundary of the pycnocline. 

8. Identify whether there is a lower mixed layer: use the same vertical profile but 
examine it from the second deepest value upward (exclude the deepest value): 

a. IF change in density (AaT) at the second deepest depth < 0.2 

b. OR IF AoT at the next depth (moving upwards, i.e. shallower) < 0.2 

c. THEN a lower mixed layer (i.e. a layer at depth where the density is not 
changing) below the pycnocline exists. 

9. If a lower mixed layer exists, then look for the lower boundary of the pycno¬ 
cline. Beginning at the second deepest value, and stepping up to the depth 
immediately below the upper pycnocline boundary, for AS and AoT values at 
each depth: 

a. IF AaT > 0.2, 

b. AND IF AS >0.1, 

c. Then this depth is the lower pycnocline boundary. 

10. If a pycnocline exists, then the upper and lower (if present) boundaries of the 
pycnocline have now been identified. 


LITERATURE CITED 

U.S. Environmental Protection Agency. 2003a. Ambient Water Quality' Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries 
(Regional Criteria Guidance) April 2003. EPA 903-R-03-002. Region III Chesapeake Bay 
Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2003b. Technical Support Documentation for Identi¬ 
fication of Chesapeake Bay Designated Uses and Attainability’. October 2003. EPA 
903-R-03-004. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004a. Ambient Water Quality> Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll afar the Chesapeake Bay and Its Tidal Tributaries - 
2004 Addendum. EPA 903-R-04-005. Region III Chesapeake Bay Program Office, 
Annapolis, MD. 

U.S. Environmental Protection Agency. 2004b. Technical Support Document for Identifica¬ 
tion of Chesapeake Bay Designated Uses and Attainability - 2004 Addendum. October 2004. 
Region III Chesapeake Bay Program Office. EPA 903-R-04-006. Annapolis, MD. 

U.S. Environmental Protection Agency. 2007a. Ambient Water Quality / Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll afar the Chesapeake Bay and Its Tidal Tributaries - 
2007 Addendum. July 2007. EPA 903-R-07-003. Region III Chesapeake Bay Program Office, 
Annapolis, MD. 

U.S. Environmental Protection Agency. 2007b. Ambient Water Quality> Criteria for Dissolved 
Oxygen, Water Clarity ; and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries 
-Chlorophyll a Addendum. October 2007. EPA 903-R-07-005. Region III Chesapeake Bay 
Program Office, Annapolis, MD. 


chapter iii 


Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria 




chapter i\/ 

Refinements to Procedures for 
Assessing Chesapeake Bay 
Water Clarity and SAV Criteria 


BACKGROUND 

With the publication of the Ambient Water Quality Criteria for Dissolved Oxygen, 
Water Clarity and Chlorophyll a for the Chesapeake Bay and its Tidal Tributaries 
(Regional Criteria Guidance) (U.S. EPA 2003a) and the Technical Support Docu¬ 
ment for Identification of Chesapeake Bay Designated Uses and Attainability 
(Technical Support Document) (U.S. EPA 2003b), the jurisdictions were provided 
with extensive guidance for how to determine attainment of the shallow-water bay 
grass designated use. Additional guidance addressing 1) water clarity criteria appli¬ 
cation periods, 2) SAV restoration acreage to shallow-water habitat acreage ratios, 3) 
SAV restoration goal acreages and 4) determining attainment of shallow-water bay 
grass use was further provided by Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and its Tidal 
Tributaries - 2004 Addendum (U.S. EPA 2004). Additional details of water clarity 
criteria and SAV restoration acreage attainment assessments were published in the 
Ambient Water Quality Criteria for Dissolved Oxygen, Water Clarity and 
Chlorophyll a for the Chesapeake Bay and its Tidal Tributaries - 2007 Addendum 
(U.S. EPA 2007). 

Since publication of the U.S. EPA 2007 Addendum, the following specific revisions 
have been agreed upon by the Chesapeake Bay Program partners: 

• Revision of the water clarity acres assessment methodology; 

• Clarification on the method for calculation of water clarity acres; 

• Clarification on the statistical model involved in converting turbidity to K d ; and 

• Development of the interpolation approach. 

Water clarity criteria and SAV restoration acreages are used to define attainment of 
the shallow-water bay grass designated use in Chesapeake Bay, its tidal tributaries 


chapter iv 


Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria 




20 


and embayments. EPA provided three measures for assessing attainment of the 
shallow-water Bay grass designated use for a Chesapeake Bay segment: 

1. measure SAV acreage from overflight data mapping analysis and compare with 
the targeted restoration goal acreage of SAV in a given segment; 

2. goal attainment may be achieved if sufficient shallow-water area with the water 
clarity necessary to achieve restoration of the targeted SAV exists, based on 
routine water quality mapping using data from the Chesapeake Bay shallow- 
water monitoring program. This measurement concept is defined as “water 
clarity acres” (see p. 54, U.S. EPA 2007); and 

3. if the water-clarity criteria were attained through the shallow-water designated 
use reaching to a specific contour (i.e., segment-specific water clarity criteria 
application depth) based on the cumulative frequency diagram assessment 
methodology, again based on shallow-water monitoring program data (U.S. 
EPA 2003a, 2003b, 2007). 

Assessment of either SAV acreage, water clarity acres, or a combination of both, 
serves as the basis for determining attainment or impairment of the shallow-water 
designated use (U.S. EPA 2007). In the absence of sufficient shallow-water moni¬ 
toring data to determine the available water clarity acres or assess water clarity 
criteria attainment using the CFD-based procedure, the EPA recommends that the 
states assess shallow-water bay grass designated use attainment based on the acres 
of mapped SAV (see Chapter 8 of U.S. EPA 2007). 


REVISION OF THE WATER CLARITY ACRES 
ASSESSMENT METHODOLOGY 

Revision of the water clarity acres assessment methodology involves clarification of 
the attainment method previously published in 2007 (U.S. EPA 2007). The 2007 
published attainment method recommended assessments to be made from a mean of 
annual means for three years of assessments (see p. 54). The revised methodology 
evaluates each year in the three-year cycle for a single best year attainment evalua¬ 
tion of segment restoration goals. This attainment assessment framework could be 
used when mapped SAV acres alone do not meet its restoration goal and as an alter¬ 
native to the CFD-based water clarity criteria assessment method (Table IV-1). 

The detailed standard operating procedures (SOPs) that define the detailed computer 
workstation methods used in each State from the import of data through data 
processing, regression calculations, interpolations and attainment assessment are 
available from Maryland and Virginia (Maryland Department of Natural Resources 
2008, Virginia Departments of Environmental Quality 2008). These specific SOPs 
are updated with computer coding revisions that maintain the standard baywide 
framework of the criteria assessment methodology but acknowledge such State 
specific issues as changes with new software and software updates, new data sources 
and programming efficiency updates to accomplish the tasks. 


chapter iv 


Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria 



21 


CLARIFICATION OF WATER CLARITY 
ASSESSMENT PROCEDURES 

U.S. EPA 2007, on pages 54-55, stated “Calculation of water clarity acres should be 
based on spatially intensive shallow-water monitoring turbidity data converted to K d , 
interpolated as described in Chapter 2 and then compared to the corresponding K d 
threshold assigned to each interpolator grid cell”. A 2007 review of the published 
language, however, found this did not correctly capture the approach to obtaining the 
K d attainment assessment when using water clarity acres. An analysis (Appendix C) 
conducted by the Chesapeake Bay Program partners shows the two methods did not 
produce dramatically different results for the selected example cruise tracks, but the 
analysis did suggest that: 

1. The originally published guidance method was simpler to conduct than this 
revised method which requires GIS-related software; 

2. The revised method predicts with slightly less error; and 

3. The revised method allows detection of spatial patterns in the individual 
parameters including better depiction of areas of uncertainty due to, for 
example, interpolation across land. 

The following revisions, which have been made by the Criteria Assessment Protocol 
Work Group under the U.S. EPA Chesapeake Bay Monitoring and Analysis Subcom¬ 
mittee, are clarifications of the published methods used by the jurisdictions for 
calculating water clarity acres. 

STATISTICAL MODEL REVISION 

The original publication of the statistical model suggested a multiplicative model of 
turbidity, chlorophyll and salinity was appropriate for converting turbidity to K d . The 
regional regressions are, however, additive multiple regression equations. The gener¬ 
alized form of such a model has been provided in Table IV-1 with an expression that 
captures the region specific coefficients, exponents involved in the root for turbidity 
and recognition of region-specific constants in accordance with what the jurisdic¬ 
tions are using to fulfill their assessments. 

Shallow-water habitat area acreage goals have been previously defined for water 
clarity acres as 2.5x each SAV acre needed to meet the SAV restoration goal acreage 
(p. 54, U.S. EPA 2007). Segment-specific SAV restoration goal acreages were previ¬ 
ously published in U.S. EPA 2003a, 2004 and 2007. 

CONVERTING TURBIDITY TO K d FOR CALCULATION OF 
WATER CLARITY ACRES 

On pages 54-55, U.S. EPA (2007) recommended “Calculation of water clarity acres 
should be based on spatially intensive shallow-water monitoring turbidity data 
converted to K d ”. To address the issue of converting turbidity measures into K d 
values, multiple regression equations were derived for determining light attenuation 


chapter iv 


Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria 



22 


Table IV-1. Revisions to Water Clarity Acres Attainment Assessment Methodology. 


Procedure 

2007 Addendum 

2008 Addendum 

Assessment calculation 

U.S. EPA 2007, p54. “Assessment of 
attaining a segment’s water clarity 
restoration acreage should be based on 
a calculation of the arithmetic mean of 
the year-by-year means of a month-by- 
month accounting of water clarity 
acres over the three year SAV growing 
season assessment period.” 

Water clarity acres for the segment are 
calculated by the taking the annual 
mean of the monthly acreage within 
the SAV growing season. Single best 
year assessments are compared with 
segment SAVAVater Clarity restoration 
acreage goals. 


Assessment calculation and 
interpolation 


U.S. EPA 2007, p54-55. “Calculation of 
water clarity acres should be based on 
spatially intensive shallow-water moni¬ 
toring turbidity data converted to K d . 
interpolated as described in Chapter 2 
and then compared to the corresponding 
K d threshold assigned to each interpo¬ 
lator grid cell.” 

U.S. EPA 2007, p80, “The very dense 
in situ measurements of turbidity from 
each sampling cruise track are first 
converted to K d . The natural log of the 
converted K d values are then interpo¬ 
lated using a standardized ordinary 
kriging procedure with ARC/GIS into 
a 25-meter square grid over the 
segments entire surface area. Once 
interpolated, the resultant interpolated 
K d values are transformed back.” 


Calculation of water clarity acres 
should be based on spatially intensive 
shallow-water monitoring data for 
turbidity, chlorophyll a and salinity in 
order to convert results to K d . Within 
each segment, the individually interpo¬ 
lated chlorophyll, turbidity, and 
salinity layer grid results are input into 
the appropriate equation on a matching 
25-m 2 cell-by-cell basis. The result of 
this cell-specific calculation of K d is 
based on region-specific multiple 
regression model equations (Table IV- 
2. Appendix D). The result is a new 
grid representing the K d surface. The 
K d grid is compared to the appropriate 
K d threshold on a cell-by-cell basis to 
create the attainment grid. The attain¬ 
ment grid results are stored in a 
database and used to calculate water 
clarity acres by initially converting cell 
counts of attainment into acreage of 
attainment inside and outside of 
current mapped SAV areas for each 
segment. As previously defined, attain¬ 
ment evaluations account for any SAV 
no grow zones by removing them 
before conducting final calculations for 
the segment (U.S. EPA 2007). Water 
clarity acres for each segment are then 
calculated by taking the annual mean 
of the monthly acreages. 


chapter iv • 


Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria 





Table IV-1. (continued). 


Procedure 


2007 Addendum 


2008 Addendum 


Statistical Modeling: Turbidity-to-Kj 
conversion 


U.S. EPA 2007. p79. Statistical 
Modeling - Model definition and 
regionally specific models. “A multiple 
regression model of K d vs. 1.5 root of 
turbidity [i.e., turbidity 171 - 5 ] x 
chlorophyll x salinity provides the best 
fit of the K d -to-turbidity relationship”. 


A multiple regression model of K d vs. 
1.5 root of turbidity [i.e., turbidity 1/1 5 ] 
+ chlorophyll a + salinity provides the 
best fit of the K d -to-turbidity 
relationship. The general form of the 
models then are K d =(x* turbidity 3 ) + 
(y*chlorophyll b ) + (z*salinity c ) + C 
where: 

• a.b and c are exponents on 
their respective water quality 
parameters and a=( 1/1.5). b=l 
and c = 1; 

• x, y and z are region-specific 
constant multipliers for the 
respective three water quality 
parameters defined in Table IV-2; 

• C is a region-specific constant; 
and 

• Turbidity is measured in NTUs, 
chlorophyll a is reported in ug/L 
and salinity measures are taken in 
parts per thousand (ppt). 


(K d ) using in situ K d calibration measurements and coincident continuous water 
quality monitoring data. A single equation for baywide application was not found to 
be appropriate (Appendix D). Rather, a series of regionally-specific multiple regres¬ 
sion models for determining light attenuation (K d ) from turbidity, chlorophyll and 
salinity data were developed (Table IV-2). Details of the regionally-specific regres¬ 
sion equation derivations supporting their application for turbidity conversion to K d 
throughout Chesapeake Bay and its tidal tributaries and embayments are docu¬ 
mented in Appendix D. 

Turbidity conversion to a K d measure is not a 1:1 unit conversion. On page 79, U.S. 
EPA (2007) specifically discussed the multiple regression model approach but 
initially provided a multiplicative form of a general equation where K d = 7.5 root of 
turbidity x chlorophyll a x salinity as providing the best fit to the K d -turbidity rela¬ 
tionship. Table IV-2 provides the updated additive form of the regression model and 
region-specific groupings of tributaries as defined through State-specific cluster 
analyses in Maryland and Virginia. Virginia-specific analyses were the first 
completed and published the use of the 1.5 root for turbidity conversion to K d (U.S. 
EPA 2007). Maryland-specific analyses showed that a 1.6 root yielded the lowest 
root mean square prediction error and highest r-square value. However, this differ¬ 
ence in root, the associated error and r-square for the 1.5 vs. 1.6 root associated with 
turbidity-K d conversion were so minor (i.e., thousandths-decimal-place differences) 
that it was decided for consistency across the jurisdictions to use the results for the 


chapter iv 


Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria 






24 


1.5 root (Appendix D). The regression equations in Table IV-2 provide regional 
groupings and their regionally appropriate coefficients. 

Note that the equations in Table IV-2 represent regions that pertain to a subset (30) 
of the 92 Chesapeake Bay assessment segments. These equations were developed 
with the best available shallow-water monitoring data throughout the Chesapeake 
Bay. As data becomes available with future monitoring applied to other segments, 
the specific groupings and their respective equations can be expected to change in 
the future as a result of new data from the unassessed regions. 

INTERPOLATION SOFTWARE AND APPROACH 

Monthly shallow water monitoring dataflow data can be imported into ArcGIS 9.2 
(ESRI 2007) map visualization software as a point dataset or as a layer in ESRI’s 
ArcMap Geostatistical Analyst Extension. A single point dataset consists of a single 
DATAFLOW cruise, typically representing a single Chesapeake Bay segment. Each 
point in the dataset has an associated measured value for chlorophyll, dissolved 
oxygen, pH, salinity, temperature, and turbidity. A cruise track typically contains 
3000-5000 points with a range of approximately 2500-6000 georeferenced locations. 
The data are generally collected from April through October with 1-2 cruises per 
month. Within a cruise dataset, duplicate data values for a georeferenced point in 
time are averaged. This is important for Arclnfo because in the present Arclnfo 
workstation environment when kriging is conducted, Arclnfo cannot work with 
duplicate points. However, kriging conducted in ArcMap’s Geostatistical Analyst has 
the capacity to deal with duplicate data and the same step is not necessary. Missing 
data are provided with an error code (e.g., Virginia uses a value of -999). 

As previously documented in Table IV-1, for the attainment assessment, U.S. EPA 
(2007, pp. 54-55) indicated “Calculation of water clarity acres should be based on 
spatially intensive shallow-water monitoring turbidity data converted to K d ”, but the 
discussion further indicates “interpolated as described in Chapter 2, and then 
compared to the corresponding K d threshold assigned to each interpolator grid cell”. 
Chapter 2 (U.S. EPA 2007 p.l 1) provided a step-by-step approach to how the inter¬ 
polation would proceed if only a single parameter is involved in the assessment (e.g., 
dissolved oxygen for dissolved oxygen attainment measures). However, turbidity is 
not equivalent to or directly translated into K d . The regionally-specific multiple 
regression model approach (see Table IV-2) requires additional steps to get from 
water quality measure to threshold assessment for attainment or impairment. 

Details of the water clarity assessment framework, including a step-by-step approach 
to assessing attainment, are provided in Appendix E. Appendix F shows 2008 Mary¬ 
land and Virginia 303d/305b Chesapeake Bay water clarity assessment results to 
provide examples of water quality criteria attainment assessment output. 


chapter iv 


Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria 


25 


Table IV-2. Regional Kd regression equations 


State-River segment Group 

Regional K d equation 

MARYLAND GROUP 1 

Bush River BSHOH, 

Gunpowder River GUNOH. 

Magothy River MAGOH. 

Middle River MIDOH. 

St. Mary’s River 1 

K d = 0.5545 + 0.3172 * Turbidity 11 ' 1 - 5 ’ + 0.0160*Chlorophyll a - 
0.0138*Salinity 

MARYLAND GROUP 2 

Eastern Bay-EASMH 

Lower Patuxent River-PAXMH 

Lower Potomac River-POTMH 

West/Rhode Rivers-WSTMH/RHDMH 

Kj = -0.1247 + 0.2820 * Turbidity (1 ' 1 ■*> + 0.0207*Chlorophyll a + 
0.0515*Salinity 

MARYLAND GROUP 3 

Fishing Bay/Chicamacomico River- 
FSH.MH. Severn River-SEVMH 

South River-SOUMH 

K d = 1.0895 + 0.4160 * Turbidity (I / , - 5) + 0.0140*Chlorophyll a - 
0.0950*Salinity 

MARYLAND GROUP 4 

Little Choptank River-LCHMH 

Miles/Wye Rivers-EASMH 

K d = -0.8991 + 0.4338 * Turbidity (1 ' 15) + 0.0180*Chlorophyll a + 
0.0912*Salinity 

MARYLAND GROUP 5 

Upper and Middle Patuxent River- 
PAXOH/PAXTF 

Kj = 0.8191 +0.2691 * Turbidity (1 '- 0.0084*Chlorophyll a + 
0.0384*Salinity 

MARYLAND GROUP 6 

Lower Chester River-CHSMH 

Middle Chester River-CHSOH 

K d = 0.0493 + 0.4658 * Turbidity (1 ' L5) + 0.0100*Chlorophyll a - 
0.0090*Salinity 

VIRGINIA GROUP 1 

Mattoponi River-MPNOHAtPNTF 

Chickahominv River-CHKOH 

James River-JMSPH JMSOH 

JMSMH JMSTF1 JMSTF2 

Appomatox River-APPTF 

K d = 1.192674757 + 0.295620722*Turbidity " ' '- 5) - 
0.056160407*Salinity + 0.000274598*Chlorophyll a 

VIRIGLNIA GROUP 2 

Upper Middle Pamunkey River-PMKOH PMKTF 
Lower York River-YRKPH YRKMH 

Lower Piankatank River-PIAMH 

Kj = 0.5275793536 + 0.3193475331 Turbidity (l ' lJ5) + 

0.0176700982*Salinity + 0.0271723238*Chlorophy 11 a 


Source: E. Perry (2006) Appendix D of this document. 

'Note: Group acronyms are a combination of river and salinity zone membership. An example is BSHOH where BSH=Bush River and 
OH=Ohgohaline zone. Salinity zones are TF=Tidal Fresh. OH=Ohgohaline. MH=Mesohaline. PH=Polyhaline. Refer to Table II-1. in 
Chapter 2 of this document for the Chesapeake Bay Program segmentation schemes. 

chapter iv • Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and 5AV Criteria 












26 


LITERATURE CITED 

Perry, Elgin. (2006). Notes on Lumping vs Splitting Kd = f(turbidity) calibration. Appendix 
D in this Addendum. 

Environmental Systems Research Institute (ESRI). 2007. ArcGIS 9.2. Redlands, CA. 

Maryland Department of Natural Resources. 2008. Water Clarity Calculation SOP 2008. 
Tidewater Ecosystem Assessment, Annapolis, MD. 

U.S. Environmental Protection Agency. 2003a. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries 
(Regional Criteria Guidance). April 2003. EPA 903-R-03-002. Region III Chesapeake Bay 
Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2003b. Technical Support documentation for identi¬ 
fication of Chesapeake Bay designated uses and attainability. October 2003. EPA 
903-R-03-004. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries - 
2004 Addendum. EPA 903-R-04-005. Region III Chesapeake Bay Program Office, 
Annapolis, MD. 

U.S. Environmental Protection Agency. 2007. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries - 
2007 Addendum. July 2007. EPA 903-R-07-003. Region III Chesapeake Bay Program Office, 
Annapolis, MD. 

Virginia Department of Environmental Quality. 2008. Water Clarity Calculation SOP 2008. 


chapter iv 


Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria 



cha pter \/ 


Chlorophyll a Criteria 
Assessment Procedures 


BACKGROUND 

Phytoplankton are small often microscopic plants floating in the water. These organ¬ 
isms form the base of the Chesapeake Bay’s food web, linking nutrients and sunlight 
energy with higher trophic levels such as fish (e.g. menhaden, bay anchovy) and with 
bottom dwelling oysters, clams and worms via primary producer and detrital path¬ 
ways. The majority of the Bay’s animals feed directly on phytoplankton or on 
organisms that directly consume the phytoplankton. Therefore, the Bay’s carry 
capacity, or its ability to produce and maintain a diversity of species, depends in 
large part on how well phytoplankton meet the needs of the consumers. 

A primary characteristic of algae is the presence of photopigments. Chlorophyll a is 
a primary photosynthetic pigment in algae and cyanobacteria (blue-green algae). 
Since chlorophyll a is a measure of photosynthetic activity, it is thus also a measure 
of the primary food source of aquatic food webs. Chlorophyll a also plays a direct 
role in reducing light penetration in shallow-water habitats, which has a direct 
impact on underwater bay grasses. Excess algae, uneaten by higher trophic level 
consumers (e.g., zooplankton, filter-feeding fish and shellfish), are decomposed by 
bacteria, and in the process, exert a biological oxygen demand upon the system. 
Decomposition of the algal organic matter through bacterial respiration can remove 
oxygen from the water column faster than it can be replaced and lead to hypoxia and 
anoxia, impairing habitat conditions for much of the Bay life. From a water quality 
perspective, chlorophyll a is the best available, most direct measure of the amount 
and quality of phytoplankton with a relationship to impacts on water clarity and 
dissolved oxygen impairments. 

The EPA originally provided the States with recommended narrative chlorophyll a 
criteria applicable to all Chesapeake Bay and tidal tributary waters: 

“Concentrations of chlorophyll a in free floating microscopic aquatic plants (algae), 
shall not exceed levels that result in ecologically undesirable consequences—such as 
reduced water clarity, low dissolved oxygen, food supply imbalances, proliferation 
of species deemed potentially harmful to aquatic life or humans or aesthetically 


chapter v • Chlorophyll a Criteria Assessment Procedures 




28 


objectionable conditions—or other render tidal waters unsuitable for designated 
uses'' (U.S. EPA 2003a). 

However, the EPA also strongly encouraged states to develop and adopt site-specific 
numerical chlorophyll a criteria for tidal waters where algal-related impairments are 
expected to persist even after the Chesapeake Bay dissolved oxygen and water 
clarity criteria have been attained. 

In Ambient Water Quality Criteria for Dissolved Oxygen, Water Clarity and Chloro¬ 
phyll a for the Chesapeake Bay and Its Tidal Tributaries - 2004 Addendum (U.S. 
EPA 2004) guidance was developed on determining where numerical chlorophyll a 
criteria should apply to Chesapeake Bay and tidal tributary waters. A general recom¬ 
mended methodology was developed by the Chesapeake Bay Program partners for 
use by the jurisdictions with tidal waters to determine consistently which local tidal 
waters will likely attain the published Chesapeake Bay dissolved oxygen and water 
clarity criteria yet show the persistence of algal-related water quality impairments. 
Examples of possible salinity-zone-specific, numerical chlorophyll a thresholds 
(pg/L) drawn from a variety of resources and approaches were provided with deri¬ 
vations based in: 

1. historical Chesapeake Bay levels; 

2. ecosystem trophic status; 

3. phytoplankton reference communities; 

4. potentially harmful algal blooms; 

5. water quality impairments; and 

6. user perceptions and State water quality standards (Table IX-1 in U.S. EPA 2004). 

From 2004 through 2006, Delaware, Maryland, Virginia and the District of 
Columbia promulgated narrative chlorophyll a criteria into their water quality stan¬ 
dards. Virginia promulgated numerical segment- and season-specific chlorophyll a 
criteria for the tidal James River. The District of Columbia promulgated numerical 
chlorophyll a criteria for its reach for the tidal Potomac River and its remaining 
waters, having previously adopted numerical criteria for chlorophyll a criteria for the 
protection of the tidal Anacostia River. 

Quantitative interpretation of Maryland’s narrative criterion for chlorophyll a is cited 
in the following excerpt from Maryland Department of the Environment’s (MDE’s) 
“Total Maximum Daily Loads of Nitrogen and Phosphorus for the Upper and Middle 
Chester River Kent and Queen Anne’s Counties, Maryland” (approved by U.S. EPA 
November 2006). The text below also describes MDE’s interpretation of this criterion 
in terms of quantified goals for application in Total Maximum Daily Loads (TMDLs). 

The Chlorophyll a level goals used in this analysis are guidelines set forth by 
Thomann and Mueller (1987) and by the EPA Technical Guidance Manual for 
Developing Total Maximum Daily Loads, Book 2, Part 1 (1997). The 
chlorophyll a narrative criteria ((COMAR 26.08.02.03-3 C (10)) states: 
“Chlorophyll a - Concentrations of chlorophyll a in free-floating microscopic 
aquatic plants (algae) shall not exceed levels that result in ecologically 
undesirable consequences that would render tidal waters unsuitable for desig¬ 
nated uses.” The Thomann and Mueller guidelines above acknowledge 
“‘Undesirable’ levels of phytoplankton [Chlorophyll a] vary considerably 

chapter v • Chlorophyll a Criteria Assessment Procedures 


29 


depending on water body.” MDE has determined per Thomann and Mueller 
(1987), that it is acceptable to maintain chlorophyll a concentrations below a 
maximum of 100 pg/L, and also to target, with some flexibility depending on 
waterbody characteristics, a 30-day rolling average of approximately 50 pg/L. 
Consistent with the guidelines set forth above, MDE’s interpretation of narra¬ 
tive criteria for chlorophyll a in the Upper and Middle Chester River consists 
of the following goals: 

E Ensure that instantaneous concentrations remain below 100 pg/1 at all 
times and 

2. Minimize exceedances of the 50 pg/1, 30-day rolling average, to a 
frequency that will not result in ecologically undesirable conditions. 

Further development of numerical chlorophyll a criteria for Chesapeake Bay tidal 
waters was advanced with the U.S. EPA 2007b publication Ambient Water Quality 
Criteria for Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake 
Bay and Its Tidal Tributaries - 2007 Chlorophyll Criteria Addendum. This 2007 
chlorophyll a criteria addendum documented the scientific basis for numerical 
chlorophyll criteria based on: 

1. historical chlorophyll a reference concentrations; 

2. chlorophyll a relationships with dissolved oxygen impairments; 

3. chlorophyll a contributions to water clarity impairments; and 

4. characteristic chlorophyll a conditions associated with specific impairments 
related to harmful algal blooms. 

Recommendations on Chesapeake Bay chlorophyll a criteria were provided and 
structured, tiered sample collection, analysis and assessment procedures were 
recommended. The specific sampling and assessment procedure recommendations 
are directed toward a harmful algal bloom (HAB) based chlorophyll a criterion that 
could be applied to the Chesapeake Bay tidal fresh and oligohaline waters. 

The basic approach used for numerical chlorophyll a criteria assessment procedure 
is documented in Table II-1 in the July 2007 criteria addendum (U.S. EPA 2007a). 
The details of the chlorophyll a criteria attainment assessment are documented 
here in Appendix G. The general application example below is illustrated for the 
James River. 


APPROACH AND PROTOCOL APPLICATION WITH EXAMPLES 

The use of spatially and temporally-intensive DATAFLOW data in conjunction with 
monthly and semi-monthly fixed station data allowed for the generation of daily 
interpolated estimates for each segment. In Virginia, during the 2008 assessment for 
example, more than 500,000 data points were used for the assessment of the three- 
year period. This monitoring approach produced data that generally resulted in from 
1 to 7 individual day-scale interpolation grids in any one month. The day-scale inter¬ 
polation grids were then used to calculate a seasonal average concentration for each 
grid cell. This approach ensures that segments are assessed with as much spatiotem- 
poral variability as possible while minimizing reliance on weak estimates stemming 
from small sample sizes. 


chapter v 


Chlorophyll a Criteria Assessment Procedures 




30 


The chlorophyll a criteria attainment assessment procedural steps are as follows: 

1. A database was compiled for the three-year assessment period containing the 
following: 

- Long-term CBP stations (records stored in CIMS database); 

- DATAFLOW verification stations (records stored in CIMS database); 

- DATAFLOW cruise-tracks (records stored by VIMS, HRSD, MD DNR); and 

- VA DEQ stations where applicable (records stored in VA DEQ CEDS database). 

2. Only data meeting appropriate QA/QC requirements are used in the assessment. 
Cruise-track data flagged with codes related to equipment failure or sampling 
artifacts were excluded, while data taken during algal blooms were used. 

3. Each segment (e.g., JMSTFL, JMSTFU, JMSOH, JMSMH, and JMSPH - 
refer to Chapter II, Table II-1 in this document for segment nomenclature and 
water body names) is interpolated separately using only the stations and cruise- 
tracks contained in them and directly adjacent. Data from a given day is 
interpolated for a segment if: 1) there were two or more fixed stations sampled 
on that day in that segment; 2) that segment was targeted by a DATAFLOW 
cruise-track on that day; or 3) there was a fixed station sampled in that segment 
and an adjacent segment was targeted by a DATAFLOW cruise-track on that 
day. The last condition takes advantage of cruise-tracks that cross over into 
multiple Chesapeake Bay Program segments. 

4. Datasets are imported into the Chesapeake Bay Interpolator and transformed 
(natural log) prior to interpolation, as chlorophyll a measurements tend to 
follow a log-normal distribution. The program defaults for search area (25 km 2 ) 
and maximum sample size (4) are used, and the “2D Inverse-Distance 
Squared’’ algorithm is chosen. The Interpolator automatically back-transforms 
interpolated estimates before creating the output files. 

5. Interpolator output was organized by segment-season-year. For each interpo¬ 
lator cell in a segment, a season-year (e.g., Spring 2005) average is calculated. 

6. For the VA example, grid-cell averages were then assessed against segment- 
season criteria specified by the VA DEQ Water Quality Assessment Guidance 
Manual for Y2008 303(d)/305(b) Integrative Water Quality Report {VA DEQ 
2007). Values over the criteria were assessed as non-attaining; those equal to or 
less than were assessed as attaining. 

7. Seasonal CFDs are generated for each segment using the steps outlined in 
Chapter 2 of Ambient Water Quality Criteria for Dissolved Oxygen , Water 
Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries - 
2007 Addendum (U.S. EPA 2007a). Assessment curves were compared 
against a default reference curve (U.S. EPA 2003). Non-attainment is calcu¬ 
lated by subtracting the area of the reference curve from the area under the 
chlorophyll a criteria assessment curve. 

TYPES OF OUTPUT 

Three types of output were produced for assessment: cumulative frequency 
distribution diagrams, maps, and tabular summaries (see Figure V-l, Figure V-2, and 
Table V-l for examples). 


chapter v 


Chlorophyll a Criteria Assessment Procedures 


31 



Figure V-1. Cumulative frequency distribution diagrams for each segment and season (Spring and Summer) showing 
the assessment curve (solid blue line) against the default reference curve (dashed black line). 



■ 1 ug/l 
10 ug/l 

§20+ ug/l 


Figure V-2. Example map graphics. Larger map shows the average chlorophyll a concentration (pg/L) in the tidal 
James River for summer 2006. Dots represent the locations of fixed stations. Inset shows the same data reduced to 
the assessment binary (grey=pass, black=fail). 


chapter v 


Chlorophyll a Criteria Assessment Procedures 


























32 


Table V-1. Example summary pass/fail results for chlorophyll a criteria assessment. 


CHLOROPHYLL CRITERIA ASSESSMENT RESULTS (2008 INTEGRATED REPORT) 


CBP Segment 

Season 

Criteria Attainment 

% Excess non-attainment 

JMSTF1 (James TF Lower) 

Spring 

Fails 

26 

JMSTF1 (James TF Lower) 

Summer 

Fails 

47 

JMSTF2 (Jmes TF Upper) 

Spring 

Fails 

27 

JMSTF2 (Jmes TF Upper) 

Summer 

Fails 

26 

JMSOH (James Oligohaline) 

Spring 

Fails 

8 

JMSOH (James Oligohaline) 

Summer 

Meets 

0 

JMSMH (James Mesohaline) 

Spring 

Fails 

17 

JMSMH (James Mesohaline) 

Summer 

Fails 

22 

JMSTPH (James Polyhaline) 

Spring 

Fails 

8 

JMSTPH (James Polyhaline) 

Summer 

Fails 

29 


FUTURE DIRECTIONS 

Extractive chlorophyll a has been shown to significantly exceed fluorescent (YSI 
probe-based) chlorophyll a measured at verification stations at times (e.g., Virginia 
James River example), therefore necessitating calibration between the two measure¬ 
ment methods. While regression coefficients were calculated so as to account for 
season and segment-specific idiosyncrasies, the goodness of fit for the different cali¬ 
bration equations varied (Table V-2). 


Table V-2. Root mean square errors for segment-season calibration regressions 
with extractive and YSI probe-based chlorophyll a measures for tidal James River 
segments 1 . 


Segment 

Season 

R 2 

RSME 

JMSTFU 

Spring 

0.59 

6.21 

JMSTFU 

Summer 

0.79 

5.51 

JMSTFL 

Spring 

0.84 

4.83 

JMSTFL 

Summer 

0.92 

6.80 

JMSOH 

Spring 

0.68 

9.54 

JMSOH 

Summer 

0.23 

4.46 

JMSMH 

Spring 

0.96 

13.28 

JMSMH 

Summer 

0.95 

9.05 

JMSPH 

Spring 

0.69 

1.84 

JMSPH 

Summer 

0.89 

2.18 


'JMSTFU=James River. Tidal Fresh Upper Segment; JMSTFL=James River. Tidal Fresh Lower 
Segment; JMSOH=James River. Oligohaline Segment; JMSMH=James River, Mesohaline Segment; 
JMSPH=James River, Polyhaline Segment 


chapter v • Chlorophyll a Criteria Assessment Procedures 























In this example, the best predictions overall were obtained in JMSPH and the worse 
predictions for JMSMH. As chlorophyll a assessments expand across the tidal 
waters, additional environmental parameter(s) may need to be used to increase the 
accuracy of estimates similar to the way K d and turbidity relationships turned to 
additive multivariate models. 

An additional consideration is that field data are interpolated without respect to land 
barriers, which can result in station data having undue influence on distant grid cells. 
The use of DATAFLOW data cruise-track points minimizes this because of the high 
density of “nearest neighbors.” However, it becomes an issue of concern when 
cruise-track points are not available in the search radius and the segment of interest 
has meandering portions (such as JMSTFU). Interpolating with barriers is not an 
option for the Bay Interpolator at this time, but ArcGIS Geostatistical Analyst for 
example provides a limited form of this functionality. 


LITERATURE CITED 

COMAR 26.08.02.03-3 C (10)). Chlorophyll a narrative criteria. 

Thomann, R. V. and J. A. Mueller. 1987. Principles of Surface Water Quality 
Modeling and Control. Harper & Row, Publ., Inc., New York, NY. 

U.S. Environmental Protection Agency. 1997. Technical Guidance Manual for 
Developing Total Maximum Daily Loads, Book 2, Part 1. EPA# 823-B-97-002. 
Office of Water, Washington, DC. 

U.S. Environmental Protection Agency. 2003. Ambient Water Quality Criteria for 
Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its 
Tidal Tributaries. April 2003. EPA 903-R-03-002. Region III Chesapeake Bay 
Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2004. Ambient Water Quality Criteria for 
Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its 
Tidal Tributaries - 2004 Addendum. October 2004. EPA 903-R-03-002. Region III 
Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2007a. Ambient Water Quality Criteria for 
Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its 
Tidal Tributaries - 2007 Addendum. July 2007. EPA 903-R-07-003. Region III 
Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2007b. Ambient Water Quality Criteria for 
Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its 
Tidal Tributaries - 2007 Chlorophyll Criteria Addendum. November 2007. EPA 903- 
R-07-005. Region III Chesapeake Bay Program Office, Annapolis, MD. 

Virginia Department of Environmental Quality (VA DEQ). 2007. Water Quality 
Assessment Guidance Manual for Y2008 303(d)/305(b) Integrative Water Quality 
Report. Water Quality Division, Office of Water Quality Programs, Virginia Depart¬ 
ment of Environmental Quality, Richmond, VA. 


chapter v 


Chlorophyll a Criteria Assessment Procedures 




34 



Acronyms 

ACB Alliance for Chesapeake Bay 

°C degrees Celcius 

CEDS Comprehensive Environmental Data System 

CFD cumulative frequency distribution 

CHLA chlorophyll a 

CIMS Chesapeake Information Management System 

CBP Chesapeake Bay Program 

DATAFLOW A field sampling technology that collects spatially intensive data (hence 
DATA) for five environmental parameters (water temperature, salinity, 
dissolved oxygen, turbidity (ntu), and fluorescence (used to estimate 
chlorophyll a) are collected from a flow-through (hence FLOW) stream of 
water collected near the surface of the water column. 


DE 

DFLO 

EPA 

GIS 

HRSD 

K d 

kg/m 3 /m 

km 2 

LICOR 

MD 

MD DNR 
m 2 

mg 0 2 /L 

NAD 

NTU 

ppt 

QA/QC 

RSME 

SAV 

SOP 

TMDL 

Mg/L 

UTM 

VA 

VA DEQ 

VIMS 

YSI 


Delaware 

DATAFLOW 

U.S. Environmental Protection Agency 
Geographic Information System 
Hampton Roads Sanitation District 
light attenuation measure 
kilograms per cubic meter per meter 
square kilometer 

Company name for a sensor used in water quality monitoring that measures underwater 
photosynthetically active radiation (PAR) 

Maryland 

Maryland Department of Natural Resources 
square meter 

milligram dissolved oxygen per liter 
North American Datum 
nephelometric turbidity units 
parts per thousand 
quality assurance/quality control 
root mean square error 
submerged aquatic vegetation 
standard operating procedures 
Total Maximum Daily Load 
micrograms per liter 
Universal Transverse Mercator 
Virginia 

Virginia Department of Environmental Quality 
Virginia Institute of Marine Science 

Yellow Springs Instruments, company producing water quality monitoring sensors 


acronyms 


appendix ^51 


Procedure for 

Assessing Dissolved Oxygen 
Criteria Attainment 

30-day Criterion, Including 
Plotting a Bioreference Curve 


Currently, dissolved oxygen is assessed using the monthly mean criterion (i.e., 30- 
day) for the open-water designated use, the monthly mean criterion for the 
deep-water designated use, and the instantaneous minimum criterion for the deep- 
channel designated use. The following step-by-step procedure is used to assess the 
status of Chesapeake Bay waters with respect to dissolved oxygen. 

STEP 1. COMPILING AND FORMATTING THE DATA SET 

A three-year dissolved oxygen dataset is compiled (most recently, the 2008 eval¬ 
uation used the 2004-2006 assessment period) with georeferenced stations for 
Chesapeake Bay Program mainstem and tributary tidal waters, and included the 
date sampled, and coincidently measured water temperature (°C) and salinity 
(ppt) covariates. Ancillary data for the same parameters were added by the state 
of Virginia where applicable, collected from their benthic monitoring program 
and the Alliance for the Chesapeake Bay’s Virginia volunteer monitoring 
program. 

A FORTRAN computer program was developed to reformat this flat file into a 
“d3d file” —a format that could be input into the Chesapeake Bay Program Inter¬ 
polator. 

STEP 2. INTERPOLATION OF WATER QUALITY MONITORING 
DATA 

For the Chesapeake Bay and its tidal tributaries and embayments, a three-dimen¬ 
sional grid-based spatial interpolator was developed to provide a common spatial 
framework for spatial extrapolation of georeferenced water quality monitoring 
data (Bahner 2001). Spatial interpolation is conducted using an inverse distance 
weighting algorithm that extrapolates point data between itself and its nearest 


appendix a 


Procedure for Assessing Dissolved Oxygen Criteria Attainment 



36 


neighbors in the spatial unit being considered. Further details regarding the basis 
of spatial interpolation of water quality monitoring data within the Chesapeake 
Bay Program segmentation framework are described in Ambient Water Quality 
Criteria for Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesa¬ 
peake Bay and Its Tidal Tributaries (Regional Criteria Guidance), pp. 154-157 
(U.S. EPA 2003). 

Recent updates to the interpolator software have been made and the present 
program is a Visual Basic program, version 4.61 August 2006, with customized 
data region and bathymetry files. 

STEP 2.1. Vertical Interpolation 

Specific to Chesapeake Bay, in areas > 12 meters, where deep-water and deep- 
channel designated uses occur, the program uses vertical depth profiles of the 
water temperature and salinity data for each Chesapeake Bay Program water 
quality monitoring station to calculate the upper and lower boundaries of the 
pycnocline. 

The program assigns the data from a particular monitoring cruise number by its 
date and divides the coincidently measured, georeferenced data into separate files 
for dissolved oxygen, salinity, and pycnocline. The result is a set of files for each 
parameter that comprise a set of files for each cruise. 

The Chesapeake Bay Program interpolator’s vertical interpolation function (On 
the Data Import screen), is run in batch mode to vertically interpolate each data 
file. The program is used with default settings beginning with a 0.5 meter START 
DEPTH and applying a 1.0 vertical meter STEP DEPTH. 

STEP 2.2. Horizontal Interpolation 

After vertical interpolation, interpolated data is available at scales below the more 
than 1.0 meter depth-steps from the water quality data collection. To generate a 
horizontal interpolation of the vertically interpolated data set, the program uses 
the Interpolate screen. Data files are again processed in batch mode presently 
using the following settings: 

3D inverse-distance squared model 
Min # Neighbors = 1 
Max # Neighbors = 4 

Horizontal Range (max) = 99000 m (essentially only limited by each 
segment’s data region) 

Vertical Range (min) = 0.1 m 
Vertical Range (max) = 0.1 m 
Vertical step size = 0.1 m 
Missing value = -9 

A file for each water quality parameter-cruise combination (parameters of 
dissolved oxygen, temperature and salinity measured coincidently in space and 


appendix a 


Procedure for Assessing Dissolved Oxygen Criteria Attainment 



time) is produced containing interpolated values for a set of cells representing the 
bathymetry of Chesapeake Bay (with depths in 1-meter increments). 

STEP 2.3. 30-Day Average Interpolations by Month 

A 30-day average is then calculated for each grid cell, for each parameter-cell 
combination. The output is a set of files for each parameter. Each set of files 
includes an individual file for each month (e.g., 30-day average interpolation 
output per month) of the three-year assessment period. 


STEP 2.4. Apportioning Results by Designated Use 

Another program uses, in this case, the 30-day average interpolated pycnocline 
and salinity files (i.e., salinity data that were originally, coincidently measured at 
the same time of the dissolved oxygen measurements) to first divide the interpo¬ 
lated dissolved oxygen data into separate files for each designated use. Second, 
the program then applies the appropriate water quality criterion based on the envi¬ 
ronmental parameter and designated use to calculate violation rates for each 
Chesapeake Bay Program assessment segment. The result is a file for each Chesa¬ 
peake Bay Program segment-designated use combination. (Note: This procedure 
of implementing different criteria over space for a segment that bridges more than 
one salinity zone reflects previous documentation in U.S. EPA 2007, Chapter IE 
Refinements to Chesapeake Bay Water Quality Criteria Assessment Methodology , 
“Step-4 - Pointwise Compliance” (pp. 17-18) and that “the only requirement (of 
the assessment) is that the final attainment determination be “yes” or “no” for 
each interpolator cell.” This procedure assures that salinity-variable criteria 
(e.g., 30-day mean = 5.5 mg 0 2 /L where salinity 0-0.5 ppt, and = 5.0 mg 0 2 /L 
where salinity > 0.5 ppt in Open Water Designated Use) are appropriately applied 
based on measured salinities during the assessment period. The Chesapeake 
Bay Program segmentation boundaries (e.g., XXXTF= “Tidal Fresh”, 
XXOH=“oligohaIine”) are not used as the salinity determinant because they are 
based on historical salinity patterns and would not accurately depict salinity 
conditions present during individual assessment periods. 


STEP 2.5. Water Quality Criteria Assessment, Attainment 
and Violations 


Output files contain a row for each month of the assessment period (2004 - 2006), 
and each row contains the following columns: 


“failed volume,” “assessed volume,” “total volume,” and 
(calculated as failed volume/assessed volume). 


“fraction failed” 


A final program takes the accumulated violation rates for each segment-desig¬ 
nated use assessment and creates a cumulative frequency distribution (CFD) 
curve. 


Criteria violation results of the assessment CFD (i.e., non-attainment) are compared 
with a standard reference or “bioreference” CFD curve, which represents an “allow¬ 
able” amount of criteria violation that can still represent a healthy habitat. For further 


appendix a 


Procedure for Assessing Dissolved Oxygen Criteria Attainment 



38 


details with illustrations of the CFD development and comparisons procedure, refer 
to Chapter vi. Recommended Implementation Procedures in Ambient Water Quality 
Criteria for Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake 
Bay and Its Tidal Tributaries (Regional Criteria Guidance) (U.S.EPA 2003). A 
review of the procedure is provided below. 

PLOTTING A BIOREFERENCE CURVE 

A biological reference curve of acceptable violation rates is generated using a cumu¬ 
lative frequency distribution (CFD) of violation rates for “healthy” designated uses. 
The violation rates are sorted in ascending order, ranked in descending order, and 
graphed on a quantile plot: 

• Violation rates are plotted on the x axis, with plotting position on the y axis. 

• Plotting position represents the probability, i/n, of being less than or equal to a 
given violation rate, or x, and is plotted on the y axis as a function of rank, or 
“i”, and sample size, or “n”. 

• The x axis is labeled “space” because the violation rate represents the fraction 
of volume that is in violation. 

• The y axis is labeled as “time” because “probability” represents the probable 
amount of time that a given violation rate will be observed. 

• The Chesapeake Bay Program currently uses the Wiebull plotting position to 
plot the cumulative distribution function. The Wiebull equation for calculating 
probability, y, for each violation rate with rank “i” is: 

• y = i/(n+l); i = rank 

In order to generate a graph of the CFD: 

• Xj , x 2 , x 3 ,...x n = violation rates provided herein, sorted in ascending order, 
with rank (i) assigned in descending order 

• yj = i/(n+l) 

• After plotting the data’s violation rates and probabilities, two additional points 
should be added to the distribution in order to complete the CFD curve: 

- Insert (x 0 ,y 0 ) = (0,1) before the first data point 

- Insert (x n+ i,y n+l ) = (1,0) after the last data point 


LITERATURE CITED 

Bahner, L. 2001. The Chesapeake Bay and Tidal Tributary Volumetric Interpolator, VOL3D, 
Version 4.0. National Oceanic and Atmospheric Administration, Chesapeake Bay Office. 
http://www.chesapeakebay.net/cims/interpolator.htm 

U. S. Environmental Protection Agency. 2003. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll afar the Chesapeake Bay and Its Tidal Tributaries. 
April 2003. EPA 903-R-03-002. Region III Chesapeake Bay Program Office, Annapolis, MD. 

U.S. Environmental Protection Agency. 2007. Ambient Water Quality Criteria for Dissolved 
Oxygen, Wlater Clarity and Chlorophyll afar the Chesapeake Bay and Its Tidal Tributaries - 
2007 Addendum, July 2007. EPA 903-R-07-003 Region III Chesapeake Bay Program Office, 
Annapolis, MD. 21403. 


appendix a 


Procedure for Assessing Dissolved Oxygen Criteria Attainment 



appendix 


Stations Involved in the 2004-2006 
303d Listing Assessment for 2008 


Table B-1. Stations involved in the 2004-2006 303d listing assessment for 2008. 


STATION.UTM_X.UTM_Y 
001,332429,4228770 
11J04.385086.4083611 
11J09.371365.4089082 
11J 16.352877.4106857 
11J20.333076.4131339 
11J24.323799.4I29435 
11J29.372088.4086261 
11M03.401227.4087648 
11M08.385305.4122864 
11M 12,396751,4128024 
11M16,391726.4150071 
11 M21,408521.4179151 
11M25.440944.4199881 
11R02.368119.4167692 
11R08.359773.4174837 
11R13,355438.4180790 
11R19.343916,4194977 
11R23.313470.4224856 
11R28.368710.4168820 
11Y01,362419.4125841 
11Y07,357536,4129365 
11Y12,352900.4139869 
11Y16,342801.4150723 
11 Y20.326135,4174280 
11 Y24.327153.4159761 
11 Y28.347570.4I45278 
12J02.333875.4124563 
12J07.381671.4084565 
12J 13,369573.4086753 
12J 18,346578.4118304 
12J23.365039.4095140 
12J28,357036.4101494 
12M01.408735,4193275 
12M05.385535.4149922 
12M 10.386931.4095357 
12M50.410360,4105475 
12M54.385679.4097040 
12M58.405548.4181450 
12M63.373122.4154180 
12R04.338172,4206191 
12R08.358718.4179540 
12R13,381556.4160738 
12R 18,366984.4165076 
12R23.363648.4172578 
12R51.366814.4163862 


STATION.UTM_X.UTM_Y 
11 JO 1,384884.4090174 
11J05.383882.4079145 
11J10,367027.4099699 
11J 17.356012.4113447 
1021,328208.4140860 
11J26.297695.4132812 
1030.377273.4091426 
11M05,399181.4092851 
11M09.382035.4130285 
11M 13,396772.4132609 
11M17.402456.4165412 
11M22.398699.4I85969 
11M26.408315.4168497 
11 R05.365769.4166464 
11R 10,358337,4176687 
11R14.353987,4185245 
11R20.336134.4203780 
11 R24.309333.4226458 
11R29.361047,4177106 
11Y02.363091,4126804 
11Y09.357344.4131741 
11Y13.349425.4144222 
11Y17,336254.4164699 
11Y21,334636.4158658 
11 Y25.325879.4161838 
12.354157,4141353 
12J04.362548.4078317 
12J 10,365836,4095861 
12J14.300363,4131717 
12J19,369509,4095185 
12J24.356556.4115037 
12J29.369379,4098616 
12M02.409776.4161842 
12M06.394259.4152752 
12M 11,392064.4095135 
12M51.410616.4095708 
12M55.403856.4144865 
12M59.430309.4194040 
12R01.348573,4188859 
12R05.356069.4182574 
12R09.368179.4165801 
12R 15,316275,4225666 
12R 19,351270.4184325 
12R26.351570.4187523 
12R52.355501.4183241 


STATION. UTM_X.UTM_Y 
11J02,378244,4095294 
11J06.387988.4077170 
11J11,360716.4094905 
1018,356104,4114426 

I 022,331320.4122437 
1027,321661,4130807 

11M01.412946.4104658 

II M06.390360.4108356 
11M 10.379684.4134839 
11 Ml4.408106,4144850 
11 Ml9,386402.4171288 
11M23,421816,4188701 
1 IM27.394095.4094153 

I IR06.364362.41653I8 
11R11.357392,4177416 
11R15,350294,4184286 
11R21,332662,4207607 
11R25.306676,4229221 
11R30.321683.4218631 
11Y04.360281,4126313 
11Y10,356379,4131943 
11Y 14,349373.4144445 
11Y18,336177,4164605 

II Y22.331819.4158177 
11Y26.346401,4146943 
129.370233.4154327 

12J05.365478.4099740 
12J11,358558.4103051 
12J 16,374202.4090672 
12J20.315601,4130865 
12J26,377346.4094970 
12J50.358181.4102588 
12M03.393520,4189833 
12M07.424142.4170262 
12M12,405266.4143616 
12M52.401836,4116605 
I2M56.394538.4I61032 
12M60.405586.4081623 
12R02,357607.4182571 
12R06.360703.4169431 
12R 10.305284.4234599 
12R 16.356829,4181047 
12R21,357887.4182198 
12R27.356876.4177033 
12 Y01.369288.4119164 


STATION.UTM_X,UTM_Y 
11J03.380998.4090425 
1008,375851.4089230 
1014,357043.4100670 
1019,356282.4116665 
1023,329429,4121882 
1028,342192.4117601 
11M02.404083.4087758 
11M07.401559,4115322 
0 Ml 1,371533.4136265 
11M15.413814.4145848 
11M20.413374,4178091 
11 M24.426484.4192946 

I IR01.372854.4163934 

II R07.361884.4168819 
11R12,358075,4178093 
11R17.347211,4188227 
11R22.315369.4224203 
11 R26.334668.4204531 
11R31,364549.4164706 
11Y06.361003.4129068 
11Y11.356647,4135524 
11Y15,346502,4145029 
11Y19,328309,4173283 
11Y23.328370.4157578 
11Y27.347570.4145278 
12J01.383101.4085048 
12J06.363363.4101423 

12J 12.364951.4081518 
12J17.380489.4088702 
12J22.308931,4130099 
12J27.340700.4119303 
12J51.331988.4138487 
12M04.409256.4112239 
12M08.408021.4116089 
12M 13,388016.4124145 
12M53,399754,4123033 
12M57,408774,4174630 
12M62.371283,4137416 
12R03,356951.4182406 
12R07.336923.4199431 
12R11.346493.4191397 
12R 17.381183.4164083 
12R22.363586.4174749 
12R50.371239.4173901 
12Y02.361474.4127273 


appendix b • Stations Involved in the 2004-2006 303d Listing Assessment for 2008 




40 


12Y03.347903.4143916 

12 Y07.366404.41240! 4 
12Y12.364919.4123685 
12Y 16.364569.4125765 
12Y20.354080.4138659 
12Y50.373193.4124020 
13J01.371864.4089215 
13J06.326134.4124003 
13J 10.357820.4100162 
13J 16,344731,4116590 
13J 20.352809.4114632 
13M01.419178.4191194 
13M05.403989.4178983 
13M10.416700,4165851 
13M 14.401684.4108566 
13R03.326246.4213702 

13R08.373787.4165129 
13R 12.345718.4189617 
13R 16.363736.4165905 
13R21.372805.4164784 

13 Y01.347639.4145194 
13Y05.336033.4159330 
13Y09.362918.4128224 
13Y 15.342473.4152260 
13Y 19.362071.4126762 
13Y24.347034.4147596 
13Y28.362930.4128983 

IAAUA004.26.294171.4256178 
1 ACOAOOl .44.371078.4204953 
I ACUT000.58.380964.4200362 
1AFOUOOO. 19.322226.4301101 
1 AHACOOO.96.383574,4198444 
1AL1FOOO. 19.319656.4286947 
1ALOGOO1.20.364727.4206164 
1 AMA0004.08.324217.4229849 
1 AMON002.60.327477.4235515 
1 ANOM007.79.349141.4215718 
1 AOCC006.99.303224.4284377 
1 APOH002.76.310660.4283722 
1 APOTO40.80.328860.4233805 
1APOT041.95.328397.4235329 
I APOT080.29.299399.4264955 
1 ASPN000.08.378093.4199326 
1 AWES000.41.363387.4210260 
1 AWLL002.21.319886.4246290 
1 AYEOOOO.92.365313.4211290 
2-BLY000.65.299746.4129267 
2-CHKO 15.50.331924.4138404 
2DAPP001.91,295841.4131825 
2DSFT001.18.289174.4129111 
2-HOFOOO.44.374870.4083960 
2-JMS007.83.374332.4087735 
2-JMS021.26.358213.4102625 
2-JMS049.00.331863.4120763 
2-JMS069.08.311660.4130270 
2-JMS074.44.302963.4132363 
2-JMS099.30.288286.4142259 
2-MIC000.03.345285.4120131 
2-PAROOO.77.383762.4073440 
2-PGN000.80.359708.4095972 
2-PGN004.57.355805.4094240 
2- PGN008.42.352010.4096905 
2-UCKOO1.23.323578.4122658 

2- WWK003.20,360896.4108595 
32.355055.4094181 

3- CRR000.23.368907.4167865 
3-HOK002.74,335485.4197499 
3-MLLOOI .31.374227.4160685 
3-RPP007.03.376681.4165415 
3-RPP019.80.363048.4174028 
3-RPP040.89.341807.4197782 


12Y04.358487.4132732 
12Y08.349722.4142221 
12Y 13.355505.4133638 
12Y17.356100.4134184 

12 Y21.369916.4122841 
12Y51.363141.4127084 
13J02.366360.4097666 
13J07.325862.4123332 
13J 11.325251.4123889 
13J 17.353021.4112633 
13J21.371166.4090850 
13M02.387927.4172675 
13M06.430545.4186091 
13M 11.432827.4196534 
13M 15.404099.4138941 
13R04.365894.4165269 
13R09.344053.4193717 

13R 13.363273.4168836 
13R 17.377456.4160510 
13R22.358895.4180498 
13Y02.355822.4133603 

13 Y06.368749.4122148 
13Y 11,351738,4138515 
13Y16.340652.4153119 
13Y20.367784.4123108 
13Y25.352660.4146795 
15,353572.4129188 

1 ABOMOOO.46.361251.4217014 
1 ACOA002.06.371715.4204176 
1 ADOUOOO.60.315541.4285375 
1 AG ADOOO.77.358568.4219508 
1 AH AM000.96.361839.4208838 
1ALIFOO1 09.319144.4288068 
1 ALOW0O4.77.354857.4217865 
1AMAW001.28.308325.4270249 
1 ANEA000.40.303460.4274805 
1AOCC002.47,306593,4279202 
1 APOHOOO.21,313983.4281771 
1 APOM002.41.296607.4246936 
1APOT041.55.328628.4234766 
1 APOTW2.01.328497.4235450 
1APREOO1.58.375458.4201092 
1 AUMC000.96.321083.4242991 
1AWES001.00.362722.4209838 
1 AXDW000.08.359594.4218429 
2-APPOO1.53.296449.4131891 
2CCHK002.40.333212.4126091 
2-CH K023.64.328494.4141696 
2-DEC000.58.383363.4069224 
2-ELI003.98.381324.4081793 
2-IND000.98.389897.4076063 
2-JMS012.79.368411.4093875 
2-JMS025.74.351588.4103152 
2-JMS050.57.329334.4121196 
2-JMS070.44.309009.4131440 
2-JMS077.70.298836.4132820 
2-JMS104.16.285959.4147513 
2-NAN000.00.369794.4086031 
2-PAROO1.77.382519.4074575 
2-PGN001.19.359035.4095644 
2-PGN005.46.354865.4094840 
2-POW000.60.342253.4121160 
2-WBE003.58.375812.4076937 

2- WWK003.98,360602.4109706 
35.281854.4155933 

3- CTM000.63,371248.4173873 
3-HOK003.61,334459.4197285 
3-MYE000.77.369169.4172488 
3-RPP011.58.369683.4164364 
3-RPP027.13.355515.4183229 
3-RPP042.12.339882.4198504 


12Y05.352816.4137062 
12Y 10.357544.4130728 
12Y 14.339717.4163580 
12Y 18.353494.4139214 
12Y22.334575.4160901 
12Y52.326533.4160948 
13J04.377311.4088797 
13J08.356303.4108389 
13J 14.348936.4119193 
13J 18.363950.4102485 
13J23.370050.4090455 
13M03.408469.4093586 
13M07.403587.4191220 
13M12.391321.4108551 
13R01.375597.4162708 
13R05.344234.4193818 
13R 10.366396.4178555 
13R 14.334998.4204036 
13R 19.369745.4167979 
13R24.342202.4198687 
13Y03.355996.4132518 
13Y07.357327.4129238 
13Y 12.352374.4139699 
13Y 17.340937.4161931 
13 Y 22.368683.4121850 
13Y26.339953.4156295 
19.371950.4115770 
1 ACHOOOl .57.297049.4264807 
1 ACOAOOl. 14.369820.4202144 
I ADOUOOl .40.314689.4286542 
1AGLE001.50.368393.4205509 
1 AHUT000.01.321740.4295553 
1 AL1S002.00.388448.4194214 
1 AMA0000.42.327977.4230477 
1 AMONOOO.96.327987.4233222 
1 ANEA000.57.303460.4274805 
1 AOCC004.52.305326.4281913 
1APOHOO1.56.312384.4282218 
1 APOT000.00.386637.4204505 
1 APOTOll .65.328608.4234936 
1 APOT042.03.328424.4235452 
1AQUA002.15.299611.4269685 
1 AUMC002.30.319481.4242280 
I AWLLOOO.94.320052,4244917 
1AXLDOOO. 15.354357.4221173 
2-APP009.52.288987.4125505 
2-CH KOO1.27.333288.4124619 
2CJMS036.83.349469.4116398 
2-DEPOOO. 26,364477.4105050 
2-GOROOO. 35.333898.4126316 
2-JMS002.14.380597.4095616 
2-JMS014.24.366977.4095460 
2-JMS027.31,353262,4110620 
2-JMS064.52.315488.4128255 
2-JMS071.56.307028.4129956 
2-JMS087.01.296140.4137110 
2-JMS 109.39.286048.4155684 
2-NAN019.14.358661.4067106 
2-PGN000.00.360272.4097165 
2-PGN002.58.357036.4095863 
2-PGN006.65.353359.4094847 
2-SBE001.53.384814.4076792 
2-W'LY002.03.386308.4090436 

2- XQW000.69.289451.4137788 

3- BRD000.62.383388.4157650 
3-HOKOOO. 15.337018.4198921 
3-LAN002.81.358359.4185514 
3-OCCOOl .85.330044.4211139 
3- R PPO13.42.366825.4163831 
3-RPP028.20.353232.4182287 
3-RPP045.21.335951.4201546 


appendix b • Stations Involved in the 2004-2006 303d Listing Assessment for 2008 


12Y06.353399.4134936 
12Y11.344249.4151126 
12Y 15.342074.4159625 
12Y 19.360933.4129972 
12Y23.355846.4134379 
13.363725.4128750 
13J05.358530.4104593 
13J09.356704.4107962 
13J 15.381042.4091999 
13J 19.379036.4088121 
13J24.352987.4116203 
13M04.403804.4106992 
13M09.399175.4116862 
13M13.392173.4187616 
13R02.375215.4164139 
13R06.362204.4175076 
13R 11.350199.4185866 
13R15.359430.4174611 
13R20.321068.4218989 
13R25.363120.4168766 
13Y04.357634.4132711 
13Y08.356584.4133882 
13Y13,354202.4134097 
13Y18.327987.4156534 
13Y23.370640.4121957 
13 Y27.346298.4144854 
1 AAUA003.71,294393,4255383 
1 ACH0003.65.294112.4266156 
1 ACOC000.42.373260.4204208 
1 ADOU002.01.314781.4286996 
1AGLE001.76.368034.4204784 
1AHUTOO1.72.319410.4296506 
1 AL1S004.20.385580.4195271 
1 AMAOOOl .36.326683.4231369 
1 AMONOOl .91.327798.4234583 
1ANOM005.99.347993.4218267 
1 AOCC006.64.303729.4284083 
1 APOH002.32.311316.4283530 
1 APOT035.00.339535.4225523 
1APOT041.80.328504.4235154 
I APOT042.72.328360.4235897 
1 AQUA002.38.299246.4269808 
1 AUMC004.43.319838.4239783 
1AWLLOOI .30.320013.4245454 
1 AYE0000.65.365901.4209979 
2-BEN001.42.368193.4080723 
2-CHK002.17.333519.4125734 
2-CLGOOO. 23.349687.4121280 
2-DSC003.19.332348.4140921 
2-GOR000.42.334075.4126537 
2-JMS006.70.374985.4090059 
2-JMS015.70.366809.4098623 
2-JMS040.93.342604.4117341 
2-J MS066.88.314358.4131632 
2-JMS073.08.305386.4132816 
2-JMS087.11.295946.4137122 
2-JOG000.62.360876.4095527 
2-PAROOO. 12.384341.4073277 
2-PGN000.76.359256.4096578 
2-PGN003.57.355670.4095516 
2-PGN007.44.352981.4095871 
2-SGL001.00.360456.4067607 

2- WAVK000.95.362876.4105498 
30.309637.4131426 

3- CRC000.15.334578.4197167 
3-HOK000.74.336555.4198261 
3-LIT000.85.366056.4179140 
3-P1SOOO. 12.339625.4196605 
3-RPPO14,38.365692.4166371 
3-RPP035.14.345177.4189616 
3- RPP060.63.324368.4219682 


41 


3-RPP067.00.320361.4223860 
3-TOT005.11.348728.4198705 
752A.346100,4112363 
7-BRK004.14.375569.4106430 
7-CHE003.49.410480.4105725 
7-CHE016.05.399854.4123004 
7-CHE025.76,376259.4113531 
7-CHE038.32.412691.4159275 
7-CHE048.79.389592.4179239 
7-COC000.06.386736,4186454 
7-COCOOO.92.387410.4187558 
7-CSX001.55,432265.4179838 
7-EBL000.01.404331.4082838 
7-EST005.56.379462,4144063 
7-EST006.91.379155.4145267 
7-HAH002.96.384656.4136541 
7-HRP001.15,384656.4136541 
7-INN001.06.386080.4184450 
7-LNCOOO.68.411015.4080454 
7-MES001.34.439853.4195280 
7-MJ B004,00.381722.4133369 
7-NSS001.62.416840.4148332 
7-OCBOOO. 18.433619.4173971 
7-OCN004.56.432524.4174183 
7-ONBOOO.56.434153.4174596 
7-PKS008.53.430317.4194030 
7-PNK010.41.373127.4154176 
7-PUN000.47.424630.4169872 
7-SEN001.35.369671.4131466 
7-THFOOO.62.434933.4185697 
7-WAR004.26.370478.4139851 
7-WES002.58,400561.4080138 
7-XANOOO. 17.387774.4187007 

7- XDN000.27.373648.4142728 

8- M PNO17.45.332556.4169260 
8-M PN039.10.314787.4184137 
8-PMK026.98.326537.4160938 
8-PMK048.80.311541.4171014 
8-YRK001.12.373185.4123996 
8-YRK009.39.363157.4127082 
ANA0082.331574.4311772 
ANA 11.329227.4305746 
ANA24.325364.4303302 
APPOO 1.83.295810.4131789 
AQU0037,, 

CB2.1.411823.4366119 
CB3.3C.382253.4317113 
CB4.1 E.380969.4297307 
CB4.2W.369343.4278281 
CB4.4.382750.4252514 
CB5.3.397329.4196671 
CB6.1,397396.4160791 
CB7.1.412737.4171156 
CB7.2E.409322.4141018 
CB7.4N.411151.4102258 
CHEO19.38,376475.4119877 
CHK015.12.331823.4137970 
CH00490., 

COR 11.369341.4172331 
COR7.373384.4178324 
CYP2.356498.4092680 
EBL002.54.405160.4079376 
EE3.0.411113.4237754 
EE3.4.430437.4195954 
EL12.380690.4082660 
ET2.1.429838.4375370 
ET4.1.420212.4344230 
ET5.2.407846.4270714 
ET8.1.428467.4221862 
FOCRE.295511.4137654 
FOR_4.307247,4229208 


3-RPP 104.47.288963,4236940 
3-URB001.00.361155.4165931 
765,302741.4273068 
7-BRN000.23.386558.4149956 
7-CHE004.52.410536.4095924 
7-CHE018.14.385697.4097079 
7-CHE027.61.403786,4144760 
7-CHE040.53.391319.4165601 
7-CHE050.87.405530.4181417 
7-COC000.86.387266.4187449 
7-COC000.95.387422.4187589 
7-CTCOOI .98.436964.4194453 
7-EBLOO 1.15.404314.4081389 
7-EST006.33.379845.4145025 
7-EST007.06.379086.4145400 
7-HKCOOO. 15.386961.4149630 
7- H UGOO1.24.414317,4141554 
7- KNS000.40.410434,4126671 
7-LOBOOl .79.405381.4077063 
7-MES006.92.445869.4192957 
7-M LF002.40.384756.4150243 
7-NWB000.34.380322.4106137 
7-OCH003.82,422522.4156417 
7-OCN004.96.433358.4174109 
7-OPCOOl.68.411815.4122598 
7-PNK000.50.384557.4155038 
7-PNK014.33.367843.4155429 
7-PUN002.12,427134.4168967 
7-SWBOO1.53,380468.4103652 
7-THG000.36.412878.4136123 
7-WAR005.77.368149,4140658 
7-WET000.60,370359.4129553 
7-X AN000.36,388059.4187099 

7- XDQ000.27,380211.4145347 

8- MPN017.46.332567,4169078 
8-PMK006.17.335019,4154703 
8-PMK028.43,324933.4161564 
8-PMK056.87.307483.4173202 
8-YRK004.79,367623,4121449 
8-YRK016.88.355361.4135297 
ANAO 1.331670.4309488 
ANA 14.328633,4305019 
ANA29.324519.4302148 
APP005.55,291190.4131674 
BBY002.88.407532.4083505 
CB2.2.398780.43559I8 
CB3.3E.383418,4317836 
CB4.1 W.373004.4297091 
CB4.3C.374994.4268540 
CB5.1,386968.4241940 
CB5.4.396587.4184289 
CB6.2,397772.4149505 

CB7.1 N.414166.4181313 
CB7.3.400035.4108423 
CB8.1.396093.4095002 
CHK001.47.334058.4124726 
CH K023.96.328514.4141669 
CHP.348307.4111102 
COR3.370468.4181758 
COR9.371191,4172745 
EBB01,389035,4077414 
EE 1.1,391609.4304585 
EE3.1.414660,4228468 
EE3.5.425661.4183574 
ERP_R1C.384450,4077457 
ET2.2,424761.4368943 
ET4.2.394661.4316568 
ET6.1,437567.4265248 
ET9.1.429117.4212609 
FOCRLAG,289043.4138148 
FRG0002.378783.4352698 


3-RPP 107.91.285821.4240552 
3-WHSOOO.89.368117.4163379 
7-BBY002.88.407537.4084005 
7-BWN000.45.405580.4081622 
7-CHE008.90.400213,4103090 
7-CHE019.79.409521.4131780 
7-CHE033.65.406763.4152802 
7-CHE046.24.408823,4174667 
7-CHE055.94.399219.4193155 
7-COC000.88,387473,4187505 
7-COCOOl.61.387408.4188526 
7-DEP001.38,434050.4180587 
7-EBL002.54,405159,4079377 
7-EST006.41,379742.4145095 
7-FER000.92.370130.4152832 
7-HKCOOO. 18.386956,4149636 
7-HUN001.88,438116.4182039 
7-LKN001.19.409794.4082039 
7-LTHOOO. 14.432103.4173642 
7-MIL002.00.384069.4183739 
7-M UD002.29.443000.4190304 
7-NWBOOO.38.380192.4106507 
7-OCNOOl .92.429087.4175978 
7-ONBOOO. 19.433549.4174366 
7-OSBOOO. 13.433132,4173695 
7-PNK001.26.384243.4153636 
7-POCOO1.76.444549.4203301 
7-QUE001.23.380963.4149699 
7-TAW000.22.418386.4157368 
7-WAROOO.88,374244.4136103 
7-WES000.62.401362,4083056 
7-WHYOOO.38,384586.4188614 

7- XBOOOl .30.405332,4074845 

8- FELOOO. 19,359370.4126447 
8-MPN021.07,331050.4173368 
8-PMKO17.67,331779.4159530 
8-PM K039.74.315366,4164047 
8-QEN002.47.353684.4129122 
8-YRK004.80.368401.4123792 
8-YRK021.16,351693,4140831 
ANA05.330420.4308527 
ANA 19.326758.4304276 
ANA30.332018.4311226 
APP007.58.290915.4128466 
BXK0031,460479,4214956 
CB3.1,393173.4345077 
CB3.3W.379813.4318075 
CB4.2C.376622.4278319 
CB4.3E.378916.4268479 
CB5.1W.379767.4242784 
CB5.4W.386042.4185908 
CB6.3.397375.4141157 
CB7.1S.406584.4159881 
CB7.3E.406515.4120769 
CB8.1E.407851.4089534 
CHK006.14,333694.4131169 
CHO0367,, 

COAN5,367507.4206156 
COR5,365898,4179190 
CR8.329492.4131109 
EBE 1,385059.4077977 
EE2.1.389081,4278722 
EE3.2,418794.4204010 
ELDO 1.381541.4080783 
ET1.1,417720,4381039 
ET2.3.422657.4373588 
ET5.0.431956.4316775 
ET6.2.422828.4243182 
FOCR27A.290992.4139076 
FOR_ 1.282966.4240883 
FRGOO18.378928.4354684 


3-RPPI 10.57,283925.4244211 
5BWNC010.02.404533.4072552 
7-BLB004.63.446343.4203032 
7-CCH000.43.409597.4124691 
7-CHE012.06.401819,4116455 
7-CHE020.80.389219,4123827 
7-CHE037.88,394538,416103l 
7-CHE047.16.425565.4176076 
7-CHS000.84.374251.4116211 
7-COC000.89,387448.4187517 
7-CRY000.59.411426.4081898 
7- DRN003.40,358389,4161011 
7-EST002.75.380641.4140383 
7-EST006.68,379536,4145280 
7-GWR008.89.375180.4192422 
7-HLD002.67.446513.4197976 
7-IND002.26.380990.4173449 
7-LKN002.77.410016.4079663 
7-LYN000.03.402732.4085142 
7-MIL004.00.381202.4184759 
7-NEW001.92,378598,4099666 
7-OCBOOO. 10.433477.4173969 
7-OCN003.28.430889,4174884 
7-ONBOOO.20.433602.4174369 
7-OSB000.25.433342.4173638 
7-PNK005.35.378919.4154933 
7- POQ004.12.371824,4111076 
7-SENOOO. 19.371294,4131339 
7-THA000.76.399755.4078069 
7-WAR002.88,371272.4137404 
7-WES001.68,401294.4081393 
7-WIL001.50,368768.4136705 

7- XDB000.08.382365.4180520 

8- KNG004.46.357578,4126404 
8-MPN024.84.326188.4173993 
8-PMK023.12,326164,4156595 
8- PM K044.64,313427,4167601 
8-SRWOOO.35.368538.4124653 
8-YRK005.67,366800,4122886 
8- YRK027.00.345614.4148057 
ANA08.329841.4307368 
ANA21,326043.4302361 
APPOO 1.53.296949,4131991 
APPO11.04.288246.4123970 
CB 1.1.407087.4377829 
CB3.2.387140.4335727 

CB4.1 C.378499.4298300 
CB4.2E.378169.4278295 
CB4.3W.369911.4268621 
CB5.2,392384.4221705 
CB5.5.395113,4172286 
CB6.4.392849.4121795 
CB7.2.404455,4141073 
CB7.4.409195.4094882 
CCM0069.421016.4255255 
CHK008.30.334117.4134513 
CHO0417,, 

COR0056,, 

COR6.368533.4175274 
CYP.356195.4093196 
EBL000.01.404330,4082838 
EE2.2,385995,4265816 
EE3.3.432666.4199634 
ELE01,384272.4078834 
ET10.1,450335,4215226 
ET3.1.423936.4357851 
ET5.1.420824.4295761 
ET7.1,430776.4235712 
FOCR APP.289197,4129039 
FOR_2,282677,4244111 
GP 1.359159,4095505 


appendix b • Stations Involved in the 2004-2006 303d Listing Assessment for 2008 



42 


GW 1.374130.4192478 
GW6.375133.4192485 
HCWF_PIER,374864.4083967 
IH3.304410.4267810 
JC1,360878.4093118 
JMS042.92.341843.4118873 
J M S055.94.323512.4126984 
JMS075.04.302135,4131894 
JMS104.16.286040.4147541 
JMSMH_20M.358354,4102271 
JMSMH_8M.352893.4104997 
JMS0H_26G.34244L4116686 
JMSPH_12P( 1 >.383298.4094382 
JMSPH_13P< 11.376002.4090772 
JMSPH_ 18P( 11.381628.4170049 
JMSPH_5P.376053.4087673 
JMSPH_9P.381693.4092832 
KNGO 1.331387.4306410 
LEI.3,369960.4244663 
LE3.1.357659.4180333 
LE3.6.386593,4161856 
LE4.3.373068.4121782 
LE5.3.368701.4094829 
LFAO 1.382889.4085496 
LKN002.77,410016.4079664 
MDR0028.377197.4352657 
MPN001.65.342334.4156035 
MPNO16.28.333757.4169416 
MPN028.86.322012.4176989 
NOM0007.376691.4351200 
PMK006.16.335150.4154852 
PMK023.69.327052.4157986 
PMK047.41.311137.4171221 
PMS29.324518,4302117 
PMS51,323525,4293226 
PNK013.91.368548.4155161 
PTBO 1.323115.4306220 
PXT0455.351147.4293900 
RET2.4.326093.4247926 
RET4.2.341294.4159764 
RIC.364854.4090657 
SC AU ST.290399.4258089 
SCSHORE.296472,4253724 
SMT02.371683,4230150 
SMT08.371055.4224610 
SMT12,374363,4224417 
TF1.5.352073,4285982 
TF2.2.316395,4284566 
TF3.1E.296531.4235512 
TF4.2.321520.4161136 
TF5.3.288218.4142278 
TF5.6.323512.4126984 
T0T_2.349762.4198971 
TRQ0088.413273.4252581 
WB 1,399240.4079616 
WE4.2.377038.4122598 
WIW0089.. 

WT 1.1.393167,4365613 
WT5.1.368360.4341015 
WT8.2.367033,4304960 
XAK7810.442840.4202056 
XBE8396.368039.4222175 
XBF523I,, 

XBF7904.. 

XCC4530.. 

XCD3596.. 

XCD7202,. 

XCF2621,371683.4230149 
XCH8973.408670.4241343 
XCJ5200,, 

XDB4544., 


GW2.372523.4194345 

GW8.379751.4190651 

HOK0005,375698.4352726 

I H4.310091.4271218 

JMS002.55.377473,4095389 

JMS043.78.341072,4121641 

JMS062.82.317509.4127379 

JMS082.49,300736.4139625 

J MS 109.62.286771,4156012 

JMSMH_23M.353723.4118661 

JMSMH_QC_0.1 N(20M 1.368947.4092187 

JMSOH_31G.329806.4120138 

JMSPH J 2P( 1001.383298.4094382 

J MSPH_ 13P( 1001.376002.4090772 

JMSPH_18P( 1001,381628.4170049 

JMSPH_5P( 11,376053.4087673 

JMSPH_9P( 1 ).381693.4092832 

KNG02,329525,4307251 

LEI.4.375738.4241396 

LE3.2.363259.4170237 

LE3.7.384553.4154548 

LE4.3B.369181.4121517 

LE5.4,376002.4090771 

LFBOI.385815.4083370 

LYN000.03,402627,4085156 

MDR0038.375899.4353341 

MPN005.04.341256.4160238 

MPN018.70.331870.4170504 

M PN031.95,317460.4180097 

PIA 1.374167,4153268 

PMK008.92.335637.4158612 

PMK034.00.321321.4160784 

PMSO 1.317531.4309771 

PMS35.323814,4299727 

PNK002.52.382214.4151860 

PNK018.35.363424.4158873 

PWC04.3 24576.4304707 

RET1.1.354868.4261571 

RET3.1.339830.4198205 

RET4.3.341892.4152809 

RICE 1,304865.4133290 

SCDOBE.299028.4247597 

SCSPILL.294502.4246711 

SMT04,373908,4227728 

SMT09.366844.4225233 

TF1.2.347994.4297623 

TF1.6.353440.4280160 

TF2.3.310717.4275538 

TF3.2.308359,4227553 

TF4.4.321476.4176991 

TF5.4.296949.4131991 

THA000.07,399520.4078978 

T0T_3,348738.4198734 

TRQO146.412737,4257964 

WBB05,375524.4076831 

WE4.3.378116,4115369 

WIW0141.439234.4243948 

WT2.1.384454,4360187 

WT6.1.372437.4326147 

WT8.3.366971.4301260 

XBD9558,. 

XBE9300.. 

XBF6734.373469.4219207 
XBF9130.372996.4223562 
XCC8346.. 

XCD3765,, 

XCE1407.. 

XCF9029.373013,4241953 
XCI3696.. 

XCJ6023,, 

XDB4877., 


GW3,373221.4192892 
GY10001.396196.4327448 
1H 1.309067.4273070 
1H5.304872.4269181 
JM SO 18.23.365742.4101843 
JMS048.03.333319.4123129 
JMS069.08.311362.4130460 
J MS094.45.292157.4139492 
JMSMHJ6M.373831.4088013 
JMSMH_25M,355353.4114480 
JMSOH_13G.331043.4120361 
JMSOH_3G.352502.4120758 
JMSPH_ 12P< 1N 1,383298.4094382 
JMSPH_13P( 1 Nl.376002.4090772 
JMSPH_18P( IN 1.381628.4170049 
JMSPH_5P( 1001.376053.4087673 
JMSPH_9P( 1001,381693.4092832 
LEI. 1.360193.4254200 
LE2.2.361327,4225505 
LE3.3.370000.4172268 
LE4.1.350343.4142679 
LE5.1.353723.4118661 
LE5.5-W.383145.4095571 
Littot.348805.419901 1 
MAT0016.308909.4270769 
M NKO146.436651.4225492 
M PN008.12,339310.4163162 
MPN021.95.329357.4173031 
MTI0015.351613.4289677 
PIS0O33.327238.4285187 
PMK012.18.333603.4159929 
PMK037.34.316238.4163086 
PMS 10,320551.4307974 
PMS37,323676.4298959 
PNK004.41.380221.4155178 
POKOO14.444014.4203910 
PXT0311.354038.4276642 
RET2.1,301859,4253019 
RET3.2.349306.4186290 
RET5.1 A.333694.4131169 
SBE2.384911.4074949 
SCH ARB.292080.4259256 
SGC0041.366758.4225326 
SMT06.372996.4223562 
SMT 10.375810.4225146 
TF1.3.351339.4297128 
TF1.7,353648.4271707 
TF2.4,302544.4267037 
TF3.2A.319860.4220341 
TF5.2,284929,4156509 
TF5.5.302135.4131894 
TORO 1.322857.4300088 
TOT_4.347298.4194598 
TSKOOO.23,348286.4142287 
WBE1.378796,4078341 
WE4.4.385118.4107873 
WIW0144.. 

WT3.1,379310.4351015 
WT7.1.369723.4318606 
WXTOOO 1.351142.4294357 
XBE2100,. 

XBF0320,, 

XBF6843.374772.4219372 
X BF9949.375810.4225146 
XCC9680,. 

XCD5599,, 

XCE2643,, 

XCH4378,409338,4232794 
XCI4078.423925.4232127 
XDA0338.. 

XDB8278.. 


GW4,373207.4191971 
HCWF_FORK,625184.4083702 
IH2,307992.4269498 
IH6.305256.4268604 
JMS032.59.353723.4118661 
JMS050.74,329875,4120262 
JMS073.37.304687.4133228 
JMS099.00.288718.4142030 
JMSMH_1A_M.370074.4094759 
JMSM H_4M,368947,4092187 
JMSOH_22G.350816.4118845 
JMSPHJ 2P.383298.4094382 
JMSPH_13P.376002.4090772 
JMSPH_ 18P.381628.4170049 
JMSPH_21 P.383125.4095549 
JMSPH_5P( IN 1.376053.4087673 
JMSPH_9P( 1 N),381693.4092832 
LE1.2.368014.4248919 
LE2.3.381705.4209059 
LE3.4.372510,4165995 
LE4.2.360117.4128260 
LE5.2.358355,4102271 
LE5.6,380766.4085121 
LKN001.19.409794.4082039 
MAT0078.315480.4273237 
MOB006.12,376827.4124418 
MPN011.97,335469,4164943 
M PN024.65,326202.4174627 
NFHFP4.306182.4134236 
PMK001.29,339868.4156069 
PMK018.13,329744,4159301 
PM K041.30.312568.4166494 
PMS21,322842.4304807 
PMS44.323103.4295980 
PNK009.96,374119.4152822 
POK0087.442045.4210439 
PXT0435.352348.4291193 
RET2.2.307375,4247179 
RET4.1,334971.4154825 
RET5.2,341843,4118873 
SBE5.384333.4070127 
SCRAVEN.294137.4250252 
SMTO 1,369552,4231904 
SMT07,373469.4219207 
SMT 11,374458.4220311 
TF1.4.351454.4292963 
TF2.1.321870,4286200 
TF3.1B,304567,4235496 
TF3.3.332405.4209585 
TF5.2A.286040.4147541 
TF5.5A.311362.4130460 
TOT_l,354273.4197480 
TPB01.328583.4306069 
TUK0022,, 

WE4.1,380697.4130313 
WESOO1.68.401294.4081393 
W1W0198., 

WT4.1,374969.4349233 
WT8.1.368570.4310484 
WXT0013,350561,4295545 
XBE6753.. 

XBF3534,, 

XBF6903,, 

XBG260L, 

XCD0517,, 

XCD6674,, 

XCF1336.373908,4227728 
XCH8097.412074.4239583 
XCI4821.415635,4233632 
XDA6515,. 

XDC3807,. 


appendix b 


Stations Involved in the 2004-2006 303d Listing Assessment for 2008 


43 


XDE4587.367132.4252270 

X DJ9007.428424.4259817 

XEA3687.309296.4270145 

XEA6046,, 

XEA9461,, 

XED0694.353759.4263780 

XEEI 502.354949.4265413 

XEE3604.355325.4269314 

XEGOI38.. 

XEG1995,, 

XEG2646.. 

XEG3623,, 

XEG4991,, 

XEG5627., 

XEG6966.. 

XEG7539,, 

XEG8519.. 

XEG8593,, 

XEH5622,. 

XEH7912., 

XEH8132., 

XE17405.. 

XFB0231.315993.4282269 

XFB0500., 

XFB 1839.317182.4285151 

XFB 1986.324071.4285164 

XFB2184.323831,4285612 

XFB5581.323512.4292014 

XFB8408.. 

XFD1283.352507.4283330 

XFG0809.. 

XFG0965., 

XFG3973,. 

XFG4620., 

XFG5054.. 

XFG9164.393576.4297449 

XFG9210.385759.4297621 

XFH2312.. 

XFH7523.402026.4294370 

XF11515,, 

XGE0284.367389.4299764 

XGE2488.368063.4303862 

XGE3275.366301.4305255 

XGE5492.368823.4309363 

XGE5984.. 

XGE6281.367272.4310889 

XGE7059.364063.4312343 

XGF0681.381515.4300351 

XGF1780.366996.4302422 

XGF5404.370593.4309383 

XGG2084.396507.4302690 

XGG3479.395865.4305355 

XGG4301.384626.4307155 

XGG4898.398663.4307823 

XGG5115.386669.4308606 

XGG5932.389110.4310075 

XGG5959.393038.4309868 

XGG6667., 

XGG8251.391935.4314323 

XGG9992.397896.4317382 

XHF0460.378756.4318542 

XHF0561.378835.4318640 

XHG0859.393086.4319043 

XHG1579.395979.4320319 

XHG6496.398583.4329224 

XHH385I,, 

XHH4528., 

XHH4742,, 

XHH4822.402236.4326309 

XHH4916,. 

XHH4931., 

XHH5046,, 

XHH6419.401908.4329334 

XIE5748.363143.4346973 

XIH0077.410272.4335782 

X1H3581.410971.4342271 

XJF0588.383342.4355530 

XJF0821.387984.4356005 

XJF2675.381393.4359748 

XJ F4289.383623.4362249 

XJG2340.390817.4358572 

XJG2718.387666.4359928 

XJG4337.390418.4362370 

XJG4451.392592.4363098 

XJG7035.390335.4367578 

XJG7856.393266.4368782 

XJH2362.. 

XJ11871,, 

YRK001.20.373779.4121033 

YRK005 40.367276.4123465 

YRK006.77.364805.4123064 

YR KOI 0.59.361480.4128917 

YRK012.78.357470.4129417 

YRKO15.09.357251.4134595 

Y R K023.40.348780.4142550 

YRK028.58.345373.4150519 

YRK031.24.341562.4152366 

ZDM0003.369412.4310776 

ZDM0000.369297.4310645 

ZDM0001.369325.4310755 

ZDM0002.369352.4310810 


appendix b • Stations Involved in the 2004-2006 303d Listing Assessment for 2008 


44 


appendix C 

A Comparison of Methods 
for Estimating K d 


The light attenuation coefficient (K d ) is used to assess the Chesapeake Bay water 
clarity criteria, measured as percent light-through-water (PLW), using the following 
equation: 

PLW = 100*exp (K d Z) 

where ‘exp’ is the base of the natural logarithms and Z’ equals the criteria applica¬ 
tion depth. 

K^j is measured in situ at DATAFLOW calibration stations using LICOR and then 
related to other measured parameters—turbidity, chlorophyll fluorescence, and 
salinity—to generate a calibration curve that enables the estimation of Kj at cruisetrack 
points. The spatially intensive nature of DATAFLOW data support the interpolative 
analysis used to produce the cumulative frequency diagram applied in criteria assess¬ 
ment. However, can be interpolated in two ways. In one method. Kj is calculated at 
each cruisetrack point using the three simultaneously measured parameters, and then 
it is interpolated. This method is described in Chapter 7 of the 2007 Ambient Water 
Quality Criteria for Dissolved Oxygen , Water Clarity and Chlorophyll afar the Chesa¬ 
peake Bay and its Tidal Tributaries - 2007 Addendum (U.S. EPA 2007). The other 
method calls for first interpolating turbidity, chlorophyll a , and salinity and then using 
the resulting estimates of these parameters to calculate Kj. This method was used by 
both Virginia Institute of Marine Science (VIMS) and Maryland Department of 
Natural Resources (MD DNR) for the 2008 water clarity assessment. 

The two methods were compared to determine if they produce similar results. 
Between 100-200 “validation” points were randomly selected and removed from 
three James River DATAFLOW cruisetracks (Figures C-l, C-2 and C-3). The 
remaining cruisetrack points were then analyzed using the two methods (i.e., calcu¬ 
lating K d from its correlated parameters prior to interpolation versus calculating K d 
after interpolating its correlated parameters). K d was calculated at each validation 
point using the turbidity, chlorophyll, and salinity measured at that point. This value 
was then compared to the estimated K d values generated from the two methods. 

The following equation (see Chapter IV, Table IV-2 of this document) was used to 
calculate K d : 

K d = 1.19267 + 0.2956*Turbidity (1/L5) - 0.05616*Salinity + 0.0002746* 
Chlorophyll a 


appendix c 


A Comparison of Methods for Estimating 


45 



Figure C-1. A comparison of the two estimates against values calculated at validation points 
(n=133) in James River tidal fresh-Lower Chesapeake Bay Program segment (JMSTFL) (4/7/2005 
cruise). 



Figure C-2. A comparison of the two 1^ estimates against values calculated at validation points 
(n = 200) in tidal middle James River Oligohaline Chesapeake Bay Program segment (JMSOH), 
5/22/2006 cruise. 


appendix c 


A Comparison of Methods for Estimating K d 


















46 



0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00 

observed K d 


Figure C-3. A comparison of the two estimates against values calculated at validation 
points (n = 99) in the lower tidal James River Polyhaline Chesapeake Bay Program segment 
(JMSPH) (9/14/2005 cruise). 


At least for the three selected cruisetracks, the two methods produced similar esti¬ 
mates, though there is a suggestion that the method used for the 2008 assessment 
predicts with less error. The methods come with their own advantages, however. The 
2007 U.S. EPA guidance method is faster and easier to do as there are fewer steps 
involved. The 2008 assessment method is difficult to do without either Arclnfo or 
Arc Spatial Analyst, but it allows one to visualize spatial patterns, particularly areas 
of uncertainty, in the individual components of K d . 


LITERATURE CITED 

U.S. Environmental Protection Agency. 2007. Ambient Water Quality Criteria for Dissolved 
Oxygen , Wlater Clarity' and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries 
- 2007 Addendum. EPA 903-R-07-003 Region III Chesapeake Bay Program Office, 
Annapolis, MD. 21403. 


appendix c 


A Comparison of Methods for Estimating K , d 









47 


a ppend ix 



Derivation of Regressions 

DATAFLOW Report on the Lumping vs. 

Splitting of Regions for MDDNR 
DATAFLOW K d vs. Turbidity Regressions 

and Calibration 1 


This report addresses a series of questions for Maryland K d -Turbidity data using a 
suite of analyses. The questions and simple answers are summarized here. More 
detailed discussion on the methods and results follow below. The following abbrevi¬ 
ations are used throughout this report text: 


ANCOVA 
Chi or chla 
Coeff Var 
DATAFLOW 

K d 

Logchla 

MD DNR 

rl_5turb 

R-square 

Root mse 

Sal 

Turb 

Trib 

VIMS 


= Analysis of covariance (ANCOVA) 

= chlorophyll a (ug/L) 

= Coefficient of Variation 

= flow through data collection system for water quality 
monitoring 

= light attenuation coefficient 
= logarithmic transformation of chlorophyll a 
= Maryland Department of Natural Resources 
= root 1.5 transformation of turbidity 
= coefficient of determination 
= root mean square error 
= salinity (ppt) 

= turbidity (ntu) 

= tributary 

= Virginia Institute of Marine Science 


'Appendix D is the report by Elgin Perry. Ph.D. 12/27/2006. Notes on Lumping vs Splitting Kd = 
f(turbidity) calibration. 


appendix d • Derivation of Regressions 



48 


xCHLA = DATAFLOW measured chlorophyll a, parameter expression 

used to differentiate it from lab derived chlorophyll a based 
on nutrient samples. 

xCHLA*tributary = interaction term of DATAFLOW measured chlorophyll a 

with tributary system 

xlnSalin = DATAFLOW instrument derived salinity measurement, 

parameter used to differentiate this data from routinely meas¬ 
ured salinity with other instrumentation. 

Questions regarding K d -Turbidity relationships: 

1. Does the 1.5 root transformation that worked well to linearize the K d - 
Turbidity relation for VIMS data work well for MD DNR data? Yes. 

2. Does one K d -Turbidity model work for all tributaries? No. 

3a. Is chla (chlorophyll a) an important predictor? Yes, but contribution is less 
than Turbidity. 

3b. Is chla effect same for all tributaries? No. 

3c. Is it better to use chla or logchla? Chla 

4. Is Salinity a useful predictor? Yes 

5. Is there a seasonal effect? Not much 

6. Can Tributaries be grouped so that calibration terms are uniform within 
group? Yes - the 15 tributaries form 6 groups. 

The following details provide the supporting analyses for the answers to the ques¬ 
tions above: 

1. Does the 1.5 root transformation that worked well to linearize the K d - 
Turbidity relation for VIMS data work well for DNR data? Yes. 

To address this question, a series of linear regression analyses were done use root 
transformations ranging from the 1.1 root to the 2.9 root. R-square and root mean 
square error for this series are reported (Table D-l) as measures of goodness of fit. 

Table D-1. R-square and root mean square error from a series of linear regression 
models where K d is the dependent variable and the independent variables include 
Tributary, root(turbidity), Tributary*root(turbidity), chlorophyll, Tribu- 
tary*chlorophyll. The root transform of turbidity ranges from 1.1 to 2.9. Note: 
“chlorophyll” refers to chlorophyll a measurements. 


appendix d 


Derivation of IQ Regressions 


49 


Table D-1. Comparison of R-square and Root Mean Square for regressions to assist 
in determining the best root transformation with turbidity. 


root RSquare RootMSE 


1.1 

0.692513 

0.777389 

1.2 

0.694958 

0.774293 

1.3 

0.696466 

0.772377 

1.4 

0.697318 

0.771292 

1.5 

0.697708 

0.770795 

1.6* 

0.697773 

0.770712 

1.7 

0.697609 

0.770921 

1.8 

0.697286 

0.771333 

1.9 

0.696852 

0.771886 

2.0 

0.696342 

0.772534 

2.1 

0.695784 

0.773244 

2.2 

0.695196 

0.773991 

2.3 

0.694590 

0.774759 

2.4 

0.693978 

0.775535 

2.5 

0.693367 

0.776310 

2.6 

0.692761 

0.777076 

2.7 

0.692165 

0.777829 

2.8 

0.691581 

0.778567 

2.9 

0.691011 

0.779286 


*Highest r-square and lowest root mean square error are obtained for the 1.6 root of turbidity. This is very nearly 
matched by the results for the 1.5 root which was optimal for the VIMS data. Thus 1.5 root will be employed for 
further work. 


2. Does one K d -Turbidity model work for all tributaries? 

Analysis of covariance (ANCOVA) with an interaction term for Tributaries*turbidity 
was used to assess the consistency of the turbidity effect over tributaries (Table D-2). 


Table D-2. ANCOVA table showing test for consistency of turbidity (turb) effect over 
tributaries. "r1_5turb" is the rootl.5 transform of turbidity measurements. 


Source 

DF 

Type III SS 

Mean Square 

F Value 

Pr > F 

tributary 

16 

19.432 

1.214 

2.04 

0.0087 

rl_5turb 

1 

330.819 

330.819 

556.82 

<.0001 

rl_5turb*tributary 

16 

65.436 

4.089 

6.88 

<.0001 


appendix d 


Derivation of Regressions 





























50 


The evidence is strong (p < 0.0001) that the coefficient for the turbidity term is not 
consistent among tributary systems. Thus some splitting of the tributaries into groups 
should be explored. 

3a. Is chla an important predictor? Yes, but contribution is less than turbidity. 

To address this question, the ANCOVA model was expanded to include terms for 
chlorophyll (as measured by DATAFLOW) and tributary*chlorophyll (Table D-3). 


Table D-3. ANCOVA table showing test for chlorophyll and consistency of chlorophyll 
effect over tributaries. 


Source 

DF 

Type III SS 

Mean Square 

F Value 

Pr > F 

tributary 

16 

19.290 

1.205 

2.39 

0.0016 

rl_5turb 

1 

197.379 

197.379 

391.14 

<.0001 

rl_5turb*tributary 

16 

25.554 

1.597 

3.17 

<.0001 

xCHLA 

1 

14.771 

14.771 

29.27 

<.0001 

xCH LA* tributary 

16 

33.057 

2.066 

4.09 

<.0001 


Both the chlorophyll term (p<0.0001) and the chlorophyll* Tributary term 
(p<0.0001) are statistically significant. However, the mean square for turbidity 
(msIII(turb) = 197.2318735) is much greater than the meansquare for chlorophyll 
(msIII(chla) = 14.7708689). From this we infer that while chlorophyll is an impor¬ 
tant predictor (p<0.0001) it is much less important than turbidity. 

3b. Is chla effect same for all tributaries? No 

The interaction statistic for chlorophyll and tributary is significant (p<0.0001) and 
this implies that the association of chlorophyll and K d is not uniform over tributaries. 

3c. Is it better to use chla or logchla? Chla 

Using the above model, the overall r 2 (chla) = 0.739471 and the overall r : (logchla) = 
0.721274. Thus is appears that the untransformed chla gives better prediction. 


4. Is Salinity a useful predictor? Yes 

Table D-4. ANCOVA table for model expanded to include salinity terms. 


Salinity appears to be an important predictor but its effect is not 
tributaries. 

Table D-4. ANCOVA table for model expanded to include salinity terms. 

consistent over 

Source 

DF 

Type III SS 

Mean Square 

F Value 

Pr > F 

tributary 

16 

16.086 

1.005 

2.03 

0.0093 

rl_5turb 

1 

162.130 

162.130 

327.82 

<.0001 

rl_5turb*tributary 

16 

14.711 

0.919 

1.86 

0.0206 

xCHLA 

1 

9.717 

9.717 

19.65 

<.0001 

xCHLA*tributary 

16 

21.609 

1.350 

2.73 

0.0003 

xInSALINITY 

1 

0.057 

0.057 

0.12 

0.7339 

x!nSALINIT*tributary 

16 

18.498 

1.156 

2.34 

0.0021 


appendix d 


Derivation of K<j Regressions 




















51 


5. Is there a seasonal effect? Not much . 

To address the seasonal issue, we compare models with and without month terms 
(Table D-5a,b,c,d). 


Table D-5a. Before adding Month and Month*trib. 


Source 

DF 

Type III SS 

Mean Square 

F Value 

Pr > F 

tributary 

16 

16.086 

1.005 

2.03 

0.0093 

rl_5turb 

1 

162.130 

162.130 

327.82 

<.0001 

rl_5turb*tributary 

16 

14.711 

0.919 

1.86 

0.0206 

Xchla 

1 

9.717 

9.717 

19.65 

<.0001 

xCH LA* tributary 

16 

21.609 

1.350 

2.73 

0.0003 

xlnSALINITY 

1 

0.057 

0.057 

0.12 

0.7339 

x!nSALINIT*tributary 

16 

18.498 

1.156 

2.34 

0.0021 


Table D-5b. Fit statistics. 


R-Square 

Coeff Var 

Root MSE 

K d l Mean 

0.748631 

31.26545 

0.703259 

2.249316 


Table D-5c. With Month and Month*Trib in the model. 


Source 

DF 

Type III SS 

Mean Square 

F Value 

Pr >F 

tributary 

16 

14.553 

0.909 

1.94 

0.0144 

Month 

6 

5.092 

0.848 

1.81 

0.0942 

Tributary*Month 

87 

62.849 

0.722 

1.54 

0.0016 

rl_5turb 

1 

93.206 

93.206 

198.72 

<.0001 

rl_5turb*tributary 

16 

16.690 

1.043 

2.22 

0.0037 

xCHLA 

1 

5.522 

5.522 

11.77 

0.0006 

xCHLA*tributary 

16 

19.573 

1.223 

2.61 

0.0005 

xlnSALINITY 

1 

0.125 

0.125 

0.27 

0.6055 

xInSALINIT*tributary 

16 

17.341 

1.083 

2.31 

0.0024 


Table D-5d. 

Fit statistics. 



R-Square 

Coeff Var 

Root MSE 

K d l Mean 

0.782748 

30.44734 

0.684857 

2.249316 


Of the two seasonal terms. Month and Trib*Month, the Month term is not significant 
(p=0.0942) and the Trib*Month term is significant (p=0.0016). The increase in r 2 is 
only about 3% which is a not a large increase for the additional 93 degrees of 
freedom in the seasonal model. The meansquares for the seasonal terms are small. 


appendix d • Derivation of Regressions 





























52 


I don’t believe there is sufficient gain from adding month to warrant the degree of 
splitting of the data that will be required by doing monthly calibration curves. 

6. Can Tributaries be grouped so that calibration terms are uniform within group? 

At this point, we have established that the model should include three useful predic¬ 
tors: turbidity, chlorophyll, and salinity. These are terms suggested by Chuck 
Gallegos of the Smithsonian Environmental Research Center, Edgewater, MD, 
(personal communication) as likely to be important. The question now is whether or 
not there are groups of tributaries where the intercept and the coefficients for these 
three predictors are fairly uniform so that they may be lumped for one calibration 
model. The coefficients are shown in Table D-6. Clearly trying to organize these into 
uniform groups is complex. To assist with this organization, a cluster analysis was 
implemented where the tributaries are the items clustered and the coefficients are the 
attributes to cluster by. Note that because some coefficients are large, but not statisti¬ 
cally significant. These data were filtered by statistical significance before clustering 
by setting all coefficients with p-value >0.1 to zero. Note for example the salinity 
coefficient for the Potomac. At 4.3, the coefficient is nearly two orders of magnitude 
greater than other salinity coefficient and yet it is not even close to being statistically 
significant (p=0.74). The sample size for the Potomac is fairly small and the salinity 
range for the data collected is also small. These factors contribute to this aberrant 
coefficient. This illustrates a hazard of splitting data into subsets that are too small. 
The results of the cluster analysis are illustrated by the dendrogram in Figure D-l. 



Figure D-1. Dendogram illustrating clustering of Maryland Tributaries by model 
coefficients. 


appendix d • Derivation of Regressions 



































53 


Table D-6. Least Square means (LSmean) and model coefficients (upper) 
and coefficient p-value (lower) for each Tributary. (TurbSlope, chlSIope and 
salSIope = regression coefficients for Turbidity, chlorophyll and salinity 
respectively; Turbpv, chlpv, salpv= p-value of model coefficient on 
Turbidity, chlorophyll and salinity). 


Obs 

Tributary 

LSmean 

TurbSlope 

Turbpv 

chlSIope 

chlpv 

salSIope 

salpv 

1 

Bush River 

0.66228760 

0.34563 

0.000000 

0.020457 

0.00000 

0.06362 

0.37814 

2 

Eastern Bay 

0.18462797 

0.36081 

0.000397 

0.007857 

0.59755 

0.04053 

0.29528 

3 

Fishing Bay/ 
Chicamacomico R. 

2.89957195 

0.35825 

0.000000 

0.019746 

0.20928 

-0.20987 

0.00001 

4 

Gunpowder River 

0.63520230 

0.29985 

0.000000 

0.018917 

0.02892 

-0.01304 

0.76646 

5 

Little Choptank 

-0.81415538 

0.45633 

0.003947 

0.041194 

0.33639 

0.07570 

0.32372 

6 

Lower Chester R 

0.11402091 

0.39440 

0.000000 

0.010186 

0.00000 

0.03962 

0.24702 

7 

Lower Patuxent 

-0.19481321 

0.29497 

0.000000 

0.016327 

0.02453 

0.06830 

0.08088 

8 

Magothy River 

0.83013497 

0.32554 

0.002151 

0.007115 

0.32672 

0.03451 

0.80087 

9 

Middle River 

0.86931693 

0.25333 

0.000000 

0.020142 

0.00732 

0.03057 

0.68222 

10 

Miles/Wye River 

-0.64846088 

0.43871 

0.000000 

0.017175 

0.00117 

0.09466 

0.05894 

11 

Potomac River T 

-0.07347812 

0.22545 

0.010952 

0.005387 

0.79545 

4.31676 

0.74414 

12 

Severn River 

1.40514400 

0.37330 

0.000148 

0.007781 

0.45731 

-0.08086 

0.53781 

13 

South River 

1.57989702 

0.19308 

0.050066 

0.023131 

0.00284 

-0.07961 

0.07244 

14 

St. Mary's Rive 

0.64822056 

0.31973 

0.000000 

0.002694 

0.48257 

0.00208 

0.93330 

15 

Upper Chester R 

0.12031682 

0.47757 

0.000000 

0.021069 

0.00006 

0.04017 

0.50339 

16 

Upper Patuxent 

0.92850523 

0.26910 

0.000000 

-0.008369 

0.06838 

0.03845 

0.26144 

17 

West/Rhode Rive 

0.36220019 

0.25795 

0.000031 

0.023594 

0.00047 

0.03514 

0.28732 


Least Squares Means at rl_5turb=0.5, xCHLA=3, xInSALINlTY=0 


The tributary groups shown in Figure D-l are a starting point for organizing the trib¬ 
utaries into groups with similar coefficients. Tributaries that are joined near the 
bottom of the distance scale have similar coefficients and the similarity decreases as 
groups are joined further up the distance scale. At the top of the distance scale, all 
tributaries are in one group. The question is “How far up the distance scale should 
the groups be formed?” For guidance in addressing this question, we implement a 
statistical criterion. We try to form tributary groups for which the three predictor 


appendix d • Derivation of Regressions 



























54 


variables have no statistically significant interaction with tributary. Starting with the 
groups shown in Figure D-l and juggling a bit, we arrive at the following groups: 

Group 1: 

Bush River 
Gunpowder River 
St. Mary’s River 
Magothy River 
Middle River 

Group 2: 

Lower Patuxent 
Potomac River 
Eastern Bay 
West/Rhode River 

Group 3: 

Severn River 
South River 

Fishing Bay/Chicamacomico 

Group 4: 

Little Choptank 
Miles/Wye River 

Group 5: 

Upper Patuxent 

Group 6: 

Lower Chester River 
Upper Chester River 

These groupings reflect a strong geographical pattern which strengthens their 
validity. The Upper Patuxent River falls in a group alone because of the negative 
coefficient for chlorophyll. This coefficient seems quite unusual when juxtaposed 
with the positive coefficients for all other tributaries. This may be the result of some 
spatial pattern that is confounded with chlorophyll and warrants additional study. 

Shown below are the ANCOVA tables for each group illustrative that the interaction 
terms lack significance (p > 0.01) (Tables D-7 thru D-16). Based on these results, we 
infer that the primary independent variables of the calibration equation: turbidity, 
salinity, and chlorophyll, have a uniform effect for each tributary group. In some trib¬ 
utary groups, some independent variables, e.g. salinity for group 1, appear to be not 
important. The model could be reformulated to omit these variables in these tribu¬ 
tary groups. 

The calibration equations for each tributary group are: 

Group 1: 

K d = 0.5545 + 0.3172*(rl_5Turb) + 0.0160*(Chlorophyll a) - 0.0138*(Salinity) 
Group 2: 

K d = -0.1247 + 0.2820*(rl_5Turb) + 0.0207*(Chlorophyll a) + 0.0515*(Salinity) 


appendix d 


Derivation of Regressions 


55 


Group 3: 

K d = 1.0895 + 0.4160*(rl_5Turb) + 0.0140*(Chlorophyll a) - 0.0950*(Salinity) 
Group 4: 

K d = -0.8991 + 0.4338*(rl_5Turb) + 0.0180*(Chlorophyll a) + 0.0912*(Salinity) 
Group 5: 

K d = 0.8191 + 0.2691 *(rl_5Turb) - 0.0084*(Chlorophyll a) + 0.0384*(Salinity) 
Group 6: 

K d = 0.0493 + 0.4658*(rl_5Turb) + 0.0100*(Chlorophyll a) - 0.0090*(Salinity) 

ANCOVA results of K d -Turbidity regression for tributary groups for Maryland Data 
Flow, (run date = December 28, 2006). 


Table D-7. Tributaries in Group 1. 

Tributary Group Tributaries 

1 Bush River 

Gunpowder River 
Magothy River 
Middle River 
St. Mary’s River 


Table D-8. ANCOVA for tributaries in Group 1. 




Sum of 




Source 

DF 

Squares 

Mean Square 

F Value 

p-value 

Model 

19 

483.54 

25.45 

47.13 

0.0000 

tributary 

4 

0.37 

0.09 

0.17 

0.9530 

rl_5turb 

1 

73.39 

73.39 

135.91 

0.0000 

rl_5turb*tributary 

4 

1.58 

0.39 

0.73 

0.5710 

xCHLA 

1 

11.09 

11.09 

20.53 

0.0000 

xCHLA*tributary 

4 

6.23 

1.56 

2.88 

0.0225 

xlnSALINITY 

1 

0.21 

0.21 

0.40 

0.5294 

xlnSALINIT*tributary 

4 

0.50 

0.12 

0.23 

0.9218 

Error 

390 

210.60 

0.54 

— 

— 

Corrected Total 

409 

694.13 

- 

- 

- 


appendix d • Derivation of Regressions 


















56 


Table D-9. Tributaries in Group 2. 


Tributary Group 

Tributaries 

2 

Eastern Bay 

Lower Patuxent 

Potomac River 

West/Rhode Rivers 


Table D-10. ANCOVA for tributaries in Group 2. 


Source 

DF 

Sum of 
Squares 

Mean Square 

F Value 

p-value 

Model 

15 

95.66 

6.38 

27.02 

0.0000 

tributary 

3 

0.34 

0.11 

0.49 

0.6930 

rl_5turb 

1 

26.26 

26.26 

111.27 

0.0000 

rl_5turb*tributary 

3 

0.61 

0.20 

0.86 

0.4634 

xCHLA 

1 

1.86 

1.86 

7.89 

0.0055 

xCHLA*tributary 

3 

0.78 

0.26 

1.10 

0.3512 

xlnSALINITY 

1 

0.06 

0.06 

0.24 

0.6259 

xInSALINIT*tributary 

3 

0.28 

0.09 

0.39 

0.7607 

Error 

192 

45.31 

0.24 

— 

— 

Corrected Total 

207 

140.97 

- 

- 

- 


Table D-11. Tributaries in Group 3. 


Tributary Group 

Tributaries 

3 

Fishing Bay/ 

Chicamacomico River 


Severn River 


South River 


appendix d 


Derivation of Regressions 




















57 


Table D-12. ANCOVA for tributaries in Group 3. 


Source 

DF 

Sum of 
Squares 

Mean Square 

F Value 

p-value 

Model 

11 

427.39 

38.85 

57.35 

0.0000 

tributary 

2 

1.12 

0.56 

0.82 

0.4410 

rl_5turb 

1 

21.28 

21.28 

31.41 

0.0000 

rl_5turb*tributary 

2 

1.34 

0.67 

0.99 

0.3736 

xCHLA 

1 

3.05 

3.05 

4.50 

0.0357 

xCHLA*tributary 

2 

0.70 

0.35 

0.51 

0.5997 

xlnSALINITY 

1 

3.19 

3.19 

4.71 

0.0317 

xInSALINIT*tributary 

2 

2.17 

1.08 

1.60 

0.2053 

Error 

141 

95.53 

0.68 

— 

_ 

Corrected Total 

152 

522.91 

- 

- 

- 


Table D-13. Tributaries in Group 4. 


Tributary Group 

Tributaries 

4 

Little Choptank River 


MilesAVye Rivers 


Table D-14. ANCOVA for tributaries in Group 4. 




Sum of 




Source 

DF 

Squares 

Mean Square 

F Value 

p-value 

Model 

7 

63.05 

9.01 

27.80 

0.0000 

tributary 

1 

0.03 

0.03 

0.09 

0.7650 

rl_5turb 

1 

13.03 

13.03 

40.20 

0.0000 

rl_5turb* tributary 

1 

0.01 

0.01 

0.02 

0.9010 

xCHLA 

1 

0.90 

0.90 

2.79 

0.0990 

xCHLA*tributary 

1 

0.15 

0.15 

0.47 

0.4939 

xlnSALINITY 

1 

1.71 

1.71 

5.28 

0.0244 

xInSALINIT*tributary 

1 

0.02 

0.02 

0.07 

0.7988 

Error 

74 

23.98 

0.32 

— 

— 

Corrected Total 

81 

87.04 

- 

- 

- 


appendix d • Derivation of Regressions 































58 


Table D-15. Tributaries in Group 6. 


Tributary Group 

Tributaries 

6 

Lower Chester Rivers 


Upper Chester Rivers 


Table D-16. ANCOVA for tributaries in Group 6. 

Source 

DF 

Sum of 
Squares 

Mean Square 

F Value 

p-value 

Model 

7 

319.82 

45.69 

69.54 

0.0000 

tributary 

1 

0.01 

0.01 

0.01 

0.9165 

rl_5turb 

1 

82.88 

82.88 

126.15 

0.0000 

rl_5turb*tributary 

1 

0.75 

0.75 

1.15 

0.2854 

xCHLA 

1 

16.64 

16.64 

25.33 

0.0000 

xCHLA*tributary 

1 

2.02 

2.02 

3.07 

0.0813 

xlnSALINITY 

1 

0.66 

0.66 

1.00 

0.3175 

x!nSALINIT*tributary 1 

0.00 

0.00 

0.00 

0.9945 

Error 

188 

123.52 

0.66 

— 

— 

Corrected Total 

195 

443.35 











appendix d • Derivation of Regressions 

















appendix 


Chesapeake Bay Water Clarity 
Assessment Framework 


STEP 1. WATER QUALITY PARAMETER INTERPOLATIONS 

Each water quality parameter in each point dataset involved in the particular region¬ 
ally specific regression model is first interpolated across the segment using the 
Ordinary kriging function in the Geostatistical Analyst included in the ArcMap soft¬ 
ware (Figure E-l). All default settings provided by Geostatistical Analyst are used in 
the interpolations, except for those specified in Table E-l. STAC 2006 (cited in U.S. 
EPA 2007) indicates that of the various types of interpolation algorithms available 
and reviewed, ordinary kriging is best positioned to address this issue, i.e., data 
density from DATAFLOW cruise tracks. 

The results of the interpolations are stored in a grid format, where each cell contains 
a value for the associated water quality parameters. For each segment, all grids used 
in this analysis are set to the exact same extent (rounded to nearest 25 m) and grid 
cell size (25 m x 25 m). This ensures that all segment grids correspond spatially 
when overlayed (Figure E-2). 

STEP 2. USING PARAMETER INTERPOLATIONS TO 
DERIVE Kd SURFACE. 

The next step towards calculating water clarity acres is to use the interpolated grids 
to calculate a K d surface. Turbidity, salinity, and chlorophyll were the three parame¬ 
ters used for determining each of the regionally-specific K d models (see Table IV-2 
in Chapter iv, also Appendix D). For each segment, the interpolated chlorophyll, 
turbidity, and salinity grids are input into the appropriate equation on a cell by cell 
basis. The result of this cell-specific calculation based on the region-specific 
multiple regression K d model is a new grid representing the K d surface. 


appendix e 


Chesapeake Bay Water Clarity Assessment Framework 





Figure E-1. Turbidity values from the dataflow point dataset (3/17/06) for the Piankatank 
River Mesohaline Chesapeake Bay Program segment (PIAMH) are used to interpolate a 
turbidity surface for the entire segment. 


Table E-1. Geostatistical analyst settings. 


Method Type 

Ordinary Kriging 

Model Type 

Spherical 

Max Sample Points 

25 / Sector 

Min Sample Points 

2 

Neighborhood Sectors 

4 


appendix e 


Chesapeake Bay Water Clarity Assessment Framework 


















/s^yyyy 




/ / / 

y y y y 


v// 


y y y 

/ / / / / 

y y 

r / // // . 

\y 

////// 

y 


Figure E-2. For each segment, all grids are 
set to the exact same extent and grid cell size 
(25 m 2 ). When the grids are overlayed to 
perform analytical functions, each cell can be 
analyzed independently. (Source ESRI 2007) 



Figure E-3. For each segment, the interpolated chlorophyll (CH), turbidity (TU), and 
salinity (SA) grids are used to generate a K d (Kd) grid. Piankatank River, Mesohaline 
Chesapeake Bay Program segment (PIAMH) example. 


appendix e 


Chesapeake Bay Water Clarity Assessment Framework 




























62 


The next step is to calculate a K d value for each cell and compare this value to a 
defined threshold. 

STEP 3. ATTAINMENT THRESHOLDS 

The following equation defines the relationship between the light attenuation coeffi¬ 
cient (K d ), PLW is the percent light through water, e is the base of the natural 
logarithms, K d is the value of the light attenuation coefficient, and Z is depth 

Equation 3: PLW = 100*exp ( K d Z) 

This equation can be used to determine the attainment thresholds at different depths 
and PLW’s (Table E-2). 


Table E-2. K d thresholds. 





Zones 


PLW 

Segment 

0-lm 

l-2m 

0-.5m* 

0.22 

Polyhaline, Mesohaline 

1.51 

0.76 

3.03 

0.13 

Oligohaline, Tidal Fresh 

2.04 

1.02 

4.08 


Each cell in the K d grid is evaluated against the appropriate ‘segment x zone’ 
threshold. For each segment, two comparisons are performed, one for each depth 
zone. For example, for Piankatank mesohaline segment, each cell must be less than 
or equal to the 1.51 threshold for zones where depth is 0-lm, and less than or equal 
to 0.76 where depth is 1-2 m. These two comparisons are merged to form an attain¬ 
ment grid (Figure E-4). Each cell in the attainment grid gets a value of one or zero, 
one if it meets the appropriate threshold and zero if it does not meet the appropriate 
threshold. Also, any designated Chesapeake Bay exclusion zones are removed from 
further analysis (Figure E-5). 

It is important to identify where this attainment is occurring in relation to other envi¬ 
ronmental factors. A code system is used to identify the presence/absence of historic 
and current SAV, and the depth zone for each cell in the grid. To determine the code 
for each cell in the grid: bathymetry, historic SAV, and current SAV are overlayed 
(Figure E-6). The resulting grid contains a representative, 3-digit code for each cell. 
The first digit indicates which bathymetric zone the cell is in, the 2nd digit desig¬ 
nates whether historic SAV is present or absent, and the last digit indicates whether 
current SAV is present or absent (Figure E-7). Finally, the attainment grid and zone 
codes are combined and the results are exported in table format to an ACCESS data¬ 
base for further analysis (Figure E-8). This method groups the attainment data by 


appendix e 


Chesapeake Bay Water Clarity Assessment Framework 







63 


unique zone codes, for example, there may be 463 cells that were in attainment for 
cells in 1-2 m of depth, where current and historic SAV are present. 

ACCESS is used to calculate water clarity acres by initially converting cell counts of 
attainment into acreage of attainment inside and outside of current SAV areas for 
each segment. Water clarity acres for the segment are then calculated by the taking 
the annual mean of the monthly acreage. Finally, the annual water clarity acreage is 
compared with the segment goals as defined in DEQ document 9 VAC 25-260 
Virginia Water Quality Standards (2005). 


_I_ 

K d Thresholds: < 1.51 (0-1 m), < ,76(1-2m) 

t 



Figure E-4. The Kd grid is compared to the appropriate «d threshold on a cell by cell basis 
to create the attainment grid. 



Figure E-5. Chesapeake Bay exclusion zones are removed from further analysis. 


appendix e 


Chesapeake Bay Water Clarity Assessment Framework 











64 



Figure E-6. Bathymetry, historic SAV, and current SAV are overlayed to determine a unique 
code that describes environmental attributes for each cell in the study area. 



Figure E-7. A representative 3-digit code for each cell is used to indicate bathymetric 
zone, historic SAV presence/absence, and current SAV presence/absence. 


appendix e 


Chesapeake Bay Water Clarity Assessment Framework 








Figure E-8. The attainment grid and zone codes are combined and the results are 
exported to an access database for further analysis. 


LITERATURE CITED 

Environmental Systems Research Institute (ESRI). 2007. ArcGIS 9.2. Redlands, CA. 

Scientific and Technical Advisory Committee (STAC). 2006. The Cumulative Frequency 
Diagram Method for Determining Water Quality Attainment: Report of the Chesapeake Bay 
Program STAC Panel to Review Chesapeake Bay Analytical Tools. STAC Publication 06- 
003. 9 October 2006. Chesapeake Bay Program Scientific and Technical Advisory 
Committee, Chesapeake Research Consortium, Edgewater, MD. 

U.S. Environmental Protection Agency. 2007. Ambient Water Quality Criteria for Dissolved 
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries - 
2007 Addendum, July 2007. EPA 903-R-07-003 Region III Chesapeake Bay Program Office, 
Annapolis, MD. 21403. 

9 VAC 25-260 Virginia Water Quality Standards. 2005. § 62.1-44.15 3a of the Code of 
Virginia. Retrieved February 21, 2007 from http://www.epa.gov/waterscience/standards/ 
wqslibrary/va/va_3_wqs.pdf 


appendix e 


Chesapeake Bay Water Clarity Assessment Framework 





66 


a ppend ix 



Water Clarity 
Attainment Results 


2008 ASSESSMENT MARYLAND WATER CLARITY 

ATTAINMENT RESULTS 

Analyses were conducted for the 2004-2006 time period, with the exception of the 
Magothy and Severn Rivers where DATAFLOW data were evaluated for the 
2001-2003 period. 

• For Maryland, the following segments had no SAV goal, therefore the analysis 
was not applicable: BACOH, CHOTF, CHSTF, NANTF, POCOH, POCTF. 

• For Maryland, the following segments passed their SAV goal in at least one 
year between 2004 and 2006: CHSOH, BSHOH, BOHOH, CB20H, PAXTF, 
NORTF, SASOH, C&DOH, PAXOH, MATTF 

• For Maryland, the following segments failed their SAV goal for each year 
between 2004 and 2006 and had incomplete or no data to perform the water 
clarity acres assessment: TANMH, CB5MH, MANMH, POCMH, CB4MH, 
NANMH, NANOH, POTTF, PISTF, POTOH, CHOMH1, POTMH, LCHMH, 
CHOMH2, CHOOH, WICMH 

• For Maryland, the following segments passed using the water clarity acres 
assessment method: GUNOH, FSBMH, SEVMH, RHDMH 

• For Maryland, the following segments failed using the water clarity acres 
assessment method: MAGMH, CHSMH, EASMH, WSTMH, PAXMH, 
SOUMH 

• For Maryland, the following segment failed due to insufficient spatial coverage 
of DATAFLOW data during its three year assessment cycle: MIDOH 

DETAILED RESULTS FOR WATER CLARITY ASSESSMENT METHOD 

The Appendix contains detailed information regarding each segment where the water 
clarity assessment method was employed. Monthly pass/fail clarity maps addition to 
the annual averages in relation to the SAV goals. Below are short narratives 


appendix f • Chesapeake Bay Water Clarity Attainment Results 



67 


describing any information that was pertinent to the assessment and possible expla¬ 
nation of why a segment passed or failed. 

GUNOH 

The Gunpowder was very close to meeting its SAV goal (2392.1 out of 2432) for 2004, 
so it was relatively easy to obtained the 99.75 water clarity acres needed to pass. 

SEVMH 

The Severn was assessed for the year 2002. During the inception of the DATAFLOW 
program in 2001 and 2002, the data on the Severn and Magothy Rivers were 
collected twice monthly from April through October. Most passing water clarity was 
observed at the southern shore near the mouth and the SAV margin areas in the 
vicinity of Round Bay. 

MIDOH 

Middle River was assessed with DATAFLOW data for 2004. In all three years of 
DATAFLOW assessment, cruise tracks were only conducted within the Middle River 
proper. The MIDOH segment however also encompasses Seneca Creek and part of 
the main-Bay to the north. During the interpolation process, water clarity acres were 
not extrapolated into the unsampled areas of Seneca Creek. This therefore meant that 
a smaller assessment area was used to try and obtain the goal for the entire segment. 
Using this method resulted in a failure of the threshold. If the water clarity pass/fail 
percentage was extrapolated to the entire shallow-water area of the segment, the 
segment would pass. 

MAGMH 

Unlike the Severn, the relatively large shallow areas at the mouth of the Magothy 
continuously failed the criteria in 2002, resulting in failure for the entire segment. 
Perhaps the orientation of the Magothy makes it more vulnerable to open Bay wave 
action and Susquehanna turbid outflow. 

CHSMH 

It should be noted that the 2004 assessment for the lower Chester relied on data from 
two separate cruises for each month. These cruised were interpolated separately. The 
general demarcation line between the two cruises was just south of the Corsica River. 
CHSMH was only at 25% of its SAV goal of 2928 acres and was therefore difficult 
to consistently obtain the large area of water clarity needed for it to pass. 


EASMH 

Eastern Bay was assessed for 2004 and only had 16.7% of its 6209 acre goal. It 
therefore had to achieve an enormous acreage of 12923.5 acres of combined acreage 
to pass. It did very well, achieving an average of 9228 acres, but not enough to pass. 


appendix f • Chesapeake Bay Water Clarity Attainment Results 


68 


SOUMH 

The South River was assessed for 2004. It had a low SAV acreage and ultimately the 
average water clarity did not even meet the SAV goal, let alone 2.5 times the goal. 

FSBMH 

Fishing Bay passed mainly on the back of its small goal and a few months producing 
good water clarity in the shoreline margins of the lower open water portions. These 
areas ultimately might not support SAV due to the high wave energy and shifting 
sediments of this segment. 

PAXMH 

Very little SAV is to be found in the lower Patuxent that also has a large SAV goal. 
Fifty-four percent of the water clarity goal was achieved. 

RHDMH/WSTMH 

The Rhode River, much like Fishing Bay, owes some of its passing success to a small 
goal (60 acres). No appreciable SAV has been observed in this segment during the 
VIMS aerial surveys. Much of the passing water clarity for the West and Rhode were 
observed in the downriver portions. This segment also contains shoreline along the 
Bay that had better estimated water clarity. The West River failed due to consistently 
bad upriver turbidity. 


2008 ASSESSMENT VIRGINIA WATER CLARITY 
ATTAINMENT RESULTS 

Analyses were conducted for the 2004-2006 time period. 

• For Virginia, the following segments had no SAV goal, therefore the analysis 
was not applicable: MPNOH, PMKOH, SBEMH, WBEMH, EBEMH, ELIPH, 
LAFMH. 

• For Virginia, the following segments passed their SAV goal in at least one year 
between 2004 and 2006: CHKOH, MPNTF, PMKTF, POTOH, POTTF, 
RPPOH, RPPTF 

• For Virginia, the following segments failed their SAV goal for each year 
between 2004 and 2006 and had no data to perform the water clarity acres 
assessment: CB5MH, CB6PH, CB7PH, CB8PH, CRRMH, LYNPH, MOBPH, 
POCMH, POTMH, RPPMH, TANMH 

• For Virginia, the following segments passed using the water clarity acres 
assessment method: CHKOH, JMSMH, JMSPH, MPNTF, PMKTF 

For Virginia, the following segments failed using the water clarity acres assessment 
method: APPTF, JMSOH, JMSTF1, JMSTF2. PIAMH, YRKMH, YRKPH 


appendix f • Chesapeake Bay Water Clarity Attainment Results 



69 


Table F-l summarizes these results of water clarity attainment results in Virginia 
segments for the single best year among the three year period of 2004 through 2006. 


Table F-1. 2008 305b/303d list segment water clarity/SAV acres attainment assessment 
results. 


CBP Segment 

Single Best Year Meets 
“SAV Acres Criteria” 

Single Best Year Meets 
“Water Clarity Acres” Criteria 

APPTF 

NO 

NO 

CB5MH 

NO 

ND 

CB6PH 

NO 

ND 

CB7PH 

NO 

ND 

CB8PH 

NO 

ND 

CHKOH 

YES 

YES 

CRRMH 

NO 

ND 

EBEMH 

YES 

ND 

JMSMH 

NO 

YES 

JMSOH 

NO 

NO 

JMSPH 

NO 

YES 

JMSTF1 

NO 

NO 

JMSTF2 

NO 

NO 

LYNPH 

NO 

ND 

MOBPH 

NO 

ND 

MPNTF 

YES 

YES 

PIAMH 

NO 

NO 

PMKTF 

YES 

YES 

POCMH 

NO 

ND 

POTMH 

NO 

ND 

POTOH 

YES 

ND 

POTTF 

YES 

ND 

RPPMH 

NO 

ND 

RPPOH 

YES 

ND 

RPPTF 

YES 

ND 

TANMH 

NO 

ND 

YRKMH 

NO 

NO 

YRKPH 

NO 

NO 

ND: No shallow-water monitoring DATAFLOW data collected during the assessment period. 


appendix f • Chesapeake Bay Water Clarity Attainment Results 







































70 


appendix 



Chlorophyll a 
Assessment Protocol 


STEP 1. CALIBRATING DATAFLOW CRUISE-TRACKS 

1. Locate the CPB segments where DATAFLOW cruise-track points are located 
using GIS. Although VIMS and Hampton Roads Sanitation District (HRSD) 
have cruise-tracks organized by segment, the ends of cruise-tracks “spill” over 
into adjacent segments. Points need to be regrouped prior to calibration since 
each segment has its own regression equation. 

2. Organize records for verification stations by segment and season (spring and 
summer). Each verification station should have extracted and YSI chlorophyll for 
each sampling date, along with turbidity and temperature data. 

3. A calibration equation should be determined for each segment-season combina¬ 
tion by calculating a log-ratio (logExtracted - logYSI) for each verification 
event, regressing it over concomitant temperature and turbidity values to deter¬ 
mine a predicted log-ratio, and multiplying the backtransformed predicted 
log-ratio by the YSI chlorophyll to estimate the extracted chlorophyll for cruise- 
track points. 

STEP 2. SETTING UP THE DATA SET 

4. Compile a chlorophyll database for the assessment period containing records 
from the following stations: 

a. Long-term CBP stations (records stored in CIMS database) 

b. DFLO verification stations (records stored in CIMS database) 

c. VA DEQ stations (records stored in VA DEQ CEDS database) 

d. DFLO cruise-tracks (records stored by VIMS/HRSD). 

5. Database should contain station name, UTM Easting and Northing coordinates 
(NAD83), laboratory-extracted chlorophyll values (ug/1), sampling date, 
sampling depth (only depths less than or equal to 1.0 m should be used), and 


appendix g 


Chlorophyll a Assessment Protocol 


71 


QA/QC comments. Fields that distinguish the project and source for each record 
should also be created, to allow for station filtering. In addition, you may also 
want to create a field for segment ID (e.g., OH, TF1, MH, etc.) for each record. 

6. In this master dataset, create a field called “input”. This will be the field that will 
be copied and pasted into the Interpolator. 

The Interpolator reads a record with the following format: 

EASTING,NORTHING,DEPTH,PARAMETER,STATION 

The “input” field should be a concatenation of the pertinent fields in your master 
database. A comma is needed between each value, so create a “comma” field that 
you reference in the concatenate formula. 

7. Replicate samples should be averaged together prior to interpolation if the time 
scale you have chosen is greater than a day. This is because the interpolator will 
automatically average multiple observations present. If the interpolator is a daily 
interpolation, the interpolator will take the replicates on that day and average 
them as is appropriate. However, if it is a monthly interpolation, and the daily 
replicates have not been previously averaged into a single value, then the repli¬ 
cates will be treated as independent observations and given undue weight in the 
monthly average. 

8. The QA/QC field should be reviewed and only data meeting appropriate QA/QC 
requirements should be used in the following interpolation steps. Cruise-track 
data associated with such codes as NQR, NNF, GPF, and GNV are to be 
excluded, while data flagged as algal blooms (CAB) should be left in. In Vir¬ 
ginia, consult the table in the Data Disclaimer and Info section of www.vecos.org 
for a description of codes. 

STEP 3. IMPORTING THE DATA INTO THE INTERPOLATOR 

9. Filter the master database so that it only shows data for the specific time period 
(e.g., March 1,2005) and from the type of stations (e.g., long-term CPB stations) 
that is desired. Fixed stations alone should be interpolated by month, while fixed 
stations + DFLO cruise-tracks should be interpolated by day. 

10. Copy and paste the “input” field into a text editor, such as Notepad. 

11. The first five lines of this text file are descriptive, providing info to both the 
reader and the Interpolator. They should look something like this: 

CHL for James March 2005 long-term CPB stations 

CHL, Chlorophyll concentration 

05/02/2005,05/25/2005 

07/10/2007:11:25 

127 

The first four lines provide general information (which would show if you gener¬ 
ated a map). The third line gives the range of sampling dates for the input data, 
and the fourth line gives the current date and time (you can put any date and 
time, but it should be formatted as shown). The fifth line is the critical one for 
the Interpolator. This is the number of data points in the input. If this number is 


appendix g • Chlorophyll a Assessment Protocol 


72 


larger than the actual number of records, an error message will be generated and 
the program will shut down. 

12. The analyst should load in all points from a cruise-track, including even the 
points beyond a segment’s boundary. Fixed station data collected on the date of 
a cruise-track should also be included in the file. 

13. Save this file with a descriptive title and save it in the same directory as the Inter¬ 
polator .exe. The program will only load files from its directory. 

14. Open the Interpolator and follow the radio buttons from left to right. Select 
James under Geography and chlorophyll concentration (two decimal places) 
under Parameter. Open your text file under Data Import (scroll down to see “All 
files”). The fields should populate automatically when the file is loaded. Then, 
click on Parameter Transformation and scroll down to “In”. Click on the Inter¬ 
polate button and select “2D Inverse-Distance Squared”. The defaults should not 
be altered. The program should then begin interpolating the data. 

15. Using Notepad or Excel, open the “.est” file that has been generated. This “esti¬ 
mates” file gives you the interpolator cells, by segment, and their estimated 
chlorophyll values. 

16. The “.log” file counts and lists the records used to interpolate each segment. 

STEP 4. AVERAGING THE DATA 

17. A seasonal average for a specific year should be determined by averaging the 
individual interpolations done on data culled from narrower time-frames within 
that season. For instance, the interpolations of daily cruise-tracks occurring 
between March and May 2005 should be averaged together to create an estimate 
for spring 2005. 

18. The Interpolator has a Math function that will average the interpolation cover¬ 
ages from individual “.est” files. The advantage of using this function is in its 
convenience, but there is one disadvantage: the program is inflexible when it 
comes to missing data. If one file has a missing value for a cell (which arises 
when there were no data points within the predefined search radius of that partic¬ 
ular cell), the Interpolator ignores the data contained in the other .est files for that 
cell, resulting in a missing value (-9) in the average output. The analyst may 
choose to bypass the Math function and do the cell-by-cell averaging in a spread¬ 
sheet, so that missing values can be replaced with blanks. After calculating the 
seasonal average, values that are still missing should be replaced with a null 
character, such as a period or an asterisk. 

19. If interpolations are based primarily on daily cruise-tracks, then averages should 
be calculated separately for each segment-year. For each segment, the assess¬ 
ment spreadsheet should use only the days of targeted DATAFLOW cruises, 
since these dates will provide good estimates for only the targeted segment. The 
only other interpolated dates that should be used in the assessment spreadsheet 
for a segment’s assessment are: 1) those with records for at least two fixed 
stations and 2) those in which an adjacent segment was targeted by a DFLO 


appendix g 


Chlorophyll a Assessment Protocol 


73 


cruise AND there is a record for at least one fixed station not located particularly 
close to the boundary of the targeted segment. 

20. The seasonal averages for each interpolator cell should then be inserted into a 
spreadsheet designated for the assessment. 

STEP 5. DESIGNING THE ASSESSMENT SPREADSHEET 

21. Set up the spreadsheet where the assessment will be done. It should have 
columns corresponding to the interpolator cells (or centroids) — either as a 
unique ID the analyst has created or as the UTMx and UTMy coordinates 
assigned to those centroids. A field containing segment identification (e.g., 
“TF1” or “PH") should also be created. The sequence of the centroids should 
match exactly with the sequence from the “.esf’ file, to allow for easy copying 
and pasting. 

22. Because only the James River main stem requires assessment, certain centroids 
need to be excluded from the analysis. It is recommended that the analyst keep 
these centroids on the spreadsheet, but that instead of being assigned a segment 
ID (e.g., “CHKOH”), they should be marked with a null character, such as a 
period or an asterisk. Along with the centroids within Appomattox and Chicka- 
hominy segments, individual JMS centroids falling in small embayments and 
non-CPB tributaries (like the Pagan River) should be restricted from the assess¬ 
ment. GIS can be used to identify these centroids. 

23. Create a field called “chlorophyll”. This is where the Interpolator estimates will 
be inserted. 

24. The next field will contain the assessment binary (“pass” or “fail”) for each 
centroid. Because each segment has a different criterion, an “IF” statement 
similar to the following should be created: 

=IF(chlorophyll=“IF(chIorophyll>criteria,“fail”,“pass”)) 

where chlorophyll = chlorophyll value for centroid 
= null value (if centroid has missing data) 
criteria = chlorophyll value the centroid is being assessed against 
fail = exceeds the criteria 
pass = less or equal to the criteria 

The statement, reduced to layman’s terms, says: “If the chlorophyll value for this 
cell is missing, insert a null value. If it’s greater than this specified value, insert 
a ‘fail’. If it’s less than or equal to this specified value, insert a ‘pass’.” 

In Virginia, refer to the table on page 35 of the Water Quality Assessment Guid¬ 
ance Manual for Y2008 for the criteria for each segment and season. 

25. A table should be created that tallies up the number of “fails” for each segment 
and calculates a percentage of “fails” from the total number of cells in a 
segment. This percentage will be used to calculate the CFD. In addition, the 
analyst should also calculate the percent of area interpolated for each segment 
by tallying up the number of null characters in the assessment field. 


appendix g • Chlorophyll a Assessment Protocol 


74 


26. Assessment spreadsheets should be created for each season (i.e., spring 2004, 
2005, 2006 and summer 2004, 2005, 2006). Spreadsheets for spring chlorophyll 
estimates should have spring assessment criteria; likewise for “summer” spread¬ 
sheets. 

STEP 6. CREATING THE CFD 

27. The percent non-attainment for each assessed segment, at each season, should be 
copied and pasted into another spreadsheet. Organize them into columns corre¬ 
sponding to segment-season. For instance, label column 1 as “TF1 spring” and 
insert all the spring percent non-attainment values for TF1 into this column. In 
the next field insert all the spring percentages for “TF2 spring”. Continue doing 
this for all segment-season combinations. These columns correspond to the “% 
space” axis on the CFD. 

28. Sort them in descending order. 

29. To generate the “% time” axis, use the following equation: 

= (100* R)/ (N+l) 

where R = rank (“ 1 ” for the first time point, “2” for the second”, “3” for the third 
and “4” for the last). 

N = number of time points. Since the assessment period consists of three season- 
years, this number is equal to 3. 

30. For each % space column, insert 100% at the top of the column and 0% at the 
bottom. For the % time column, insert 0% at the top of the column and 100% at 
the bottom. 

31. You can now create the assessment curve. 

32. To calculate % space for the 10% reference curve, use the following equation: 
% space = [a/(y+b)] - b 

where y = % time 
b = 0.042995 
a = b 2 + b 

33. You can now create the 10% reference curve. 

STEP 7. CALCULATING THE PERCENT EXCESS NON-ATTAINMENT 

34. Convert the percentage axes of the CFD to fractional axes for this calculation. 

35. The trapezoidal rule should be applied to both assessment and reference curves 
to determine the area underneath each curve. The following website describes 
how to do the calculations using MS Excel: www.montanamath.org/ 
TMME/v4n 1 /TMMEv4n 1 a6.pdf 

36. Subtract the area under the assessment curve from the reference curve, looking 
only at the parts of the assessment curve that go beyond the reference curve. 

37. Multiply the value by 100. This number represents “% excess non-attainment”. 


appendix g 


Chlorophyll a Assessment Protocol 







LC ACQUISITIONS 



0 030 "338 388 8 



U.S. Environmental Protection Agency 
Region III 

Chesapeake Bay Program Office 
Annapolis, Maryland 
1-800-YOUR-BAY 

and 

Region III 

Water Protection Division 
Philadelphia, Pennsylvania 

in coordination with 

Office of Water 

Office of Science and Technology 
Washington, D.C. 

and 

the states of 

Delaware, Maryland, New York, 
Pennsylvania, Virginia and 
West Virginia and the District of Columbia 


Ambient Water Quality Criteria for Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries—2008 Technical Support for Criterial Assessment Addendum 











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